A Structured, Tagged, and Localized Visual Question Answering Dataset with Full Sentence Answers and Scene Graphs for Chest X-ray Images
Authors: Philip Müller
Friederike Jungmann
Georgios Kaissis
Daniel Rueckert
Venue: ICLR 2026 Poster (2026)
Keywords: VQA
Localization
Vision-Language Modeling
Medical Imaging
Chest X-Rays
Scene Graphs
Primary area: datasets and benchmarks
TL;DR: We present a large-scale CXR VQA dataset derived from MIMIC-CXR with 42M QA pairs,featuring multi-part answers,bounding boxes,and structured tags; it was generated using LLM-based extraction from radiology reports and localization models.

Abstract: Visual Question Answering (VQA) enables targeted and context-dependent analysis of medical images, such as chest X-rays (CXRs). However, existing VQA datasets for CXRs are typically constrained by simplistic and brief answer formats, lacking localization annotations (e.g., bounding boxes) and structured tags (e.g., region or radiological finding/disease tags). To address these limitations, we introduce MIMIC-Ext-CXR-QBA (abbr. CXR-QBA), a large-scale CXR VQA dataset derived from MIMIC-CXR, comprising 42 million QA-pairs with multi-granular, multi-part answers, detailed bounding boxes, and structured tags. 
We automatically generated our VQA dataset from scene graphs (also made available), which we constructed using LLM-based information extraction from radiology reports. After automatic quality assessment, we identified 31M pre-training and 7.5M fine-tuning grade QA-pairs, providing the largest and most sophisticated VQA dataset for CXRs to date. Tools for using our dataset and the construction pipeline are available at https://github.com/philip-mueller/mimic-ext-cxr-qba/ .

Published as a conference paper at ICLR 2026
A STRUCTURED, TAGGED, AND LOCALIZED VISUAL QUESTION ANSWERING DATASET WITH FULL SEN- TENCE ANSWERS AND SCENE GRAPHS FOR CHEST X- RAY IMAGES
Philip Müller! Friederike Jungmann Georgios Kaissis Daniel Rueckert
Chair for AI in Healthcare and Medicine,
Technical University of Munich (TUM) and TUM University Hospital, Germany
2 Department of Diagnostic and Interventional Radiology, TUM University Hospital, Germany
3 Hasso Plattner Institute for Digital Engineering, University of Potsdam, Germany
4 Department of Computing, Imperial College London, UK
5 Munich Center for Machine Learning (MCML), Germany
ABSTRACT
Visual Question Answering (VQA) enables enables targeted and context-dependent analysis of medical images, such as chest X-rays (CXRs). However, existing VQA datasets for CXRs are typically constrained by simplistic and brief answer formats, lacking localization annotations (e.g., bounding boxes) and structured tags (e.g., region or radiological finding/disease tags). To address these limitations, we introduce MIMIC-ExI-CO -EE-EE----R-B.. (abbr. EXR-QBA), large-scale CXR VQA dataset derived from MIMIC-CXR, comprisin 42 million QA-pairs with multi-granular, multi-part answers, detailed bounding boxes, and structured tags. We automatically generated our VQA dataset from scene graphs (also made available), which we constructed using LLLM-basee information extraction from radiology reports. After automatic quality assessment, we identified 31M pre- pre-training and 7.5M fine-tuning grade QA-pairs, providing the largest and most sophisticated VQA dataset for CXRs to date. Tools for using our dataset and the construction pipeline are available at /tttp::/gittiin cwm/ p ww-mupplllllleemm muel eer mi mi c ex-ex----- -et-exx--xxaao -xr-
INTRODUCTION
With the emergence of Large Language Models (LLMs) and Large Multimodal Models (LMMs), interactive and conversational tasks have gained popularity in medical image analysis, particularly in the context chest X-ray (CXR) interpretation (Chen et al.) 2024] Müller et al.) 22255] Tu et al.) 2024 Xie et al., 2025). A prominent example of such interactive tasks is Visual Question Answering (VQA), where a model is presented with an image and a corresponding textual question, and tasked with generating an answer. Unlike conventional medical imaging approaches, which always produce the same output (such as classification labels, bounding boxes, or textual reports) for a given image, VQA enables users interactively explore and interpret images in context-dependent manner. Training robust VQA models for medical applications requires high-quality, large-scale scale training datasets. Existing CXR VQA datasets suffer from several limitations: (i) they often contain only short and simplistic answers, (ii) they lack localization information (such as bounding boxes), and (iii) they provide little structured metadata (e.g., region and finding/disease annotations, or uncertainty estimates). Additionally, their relatively small size constrains their utility for pretraining.
To address these challenges, we propose a pipeline for automatic VQA dataset creation and apply to construct a new large-scale CXR VQA dataset. Unlike prior datasets, each question-answer (QA) pair includes multi-granulaat multi-pan answers composed full sentences in the style of radiology reports. Furthermore, (e.g., findings and regions), enhancing interpretability and facilitating the development of more advanced and transparent medical VQA models. Fig. shows examples of our generated QA-pairs.
dataset provides detailed bounding boxes and additional structured tags

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Figure 1: Examples of question-answwr weesse (QA) pairs for each of our four different types of questions.
For each question (for given chest X-ray), detailed answer with sentences in the style of free-text radiology reports is given, supplemented by bounding boxes (for both positive and negative answers), and set of tags (e.g. regions, findings, certainty, etc.). For more examples, we refer to Sec. A
Our contributions are as follows:
We propose an automatic dataset construction pipeline for large-scale medical VQA datasets, offering structured, tagged, and localized answers. To provide reliable data source for this pipeline, we develop an automatic ceeneggrapl construction method.
Building on this, we introduce MIMIC-Exs ExI-CCEXXXX------ QBA (abbr. CXR-QBA), CXR VQA dataset of 42M question-answer pairs derived from MIMIC-CXR (Johnson et al. 2024b).
We automatically assess the quality of the generated QA-pairs, identifying 31 22 pairs as pre-training (PT) grade and 7.5M of these as fine-tuning (FT) grade.
We perform extensive quality controls, including validations against expert annotations and assessment the automatic ratings, ensuring the robustness our pipeline.
We introduce the structured VQA task for CXRs and provide a baseline model to demonstrate our dataset's 's utility and guide future model development.
RELATED WORK
VQA Datasets for Chest X-Rays VQA datasets (shown Tab. D are scarce the medical imaging domain, with most notable examples being VQA-RAD (Lau et al. 2018) and SLAKE (Liu et al. 2021), 1, which hand-labeled but limited in size. On the other hand, VQA-Med at ImageCLEF 2019 (Ben Abacha et al., 2019) was automatically constructed using QA tem- plates based on image annotations, which may limit its answer quality. To improve the quality, PMC-VOL (Zhang et al. (024) used an LLM to generated QA-pairs based on provided captions. VQA datasets for chest X-rays include MIMIC-EX--MIMICCCCNE -XA- (Bae) et al. 2024) and Medical-CSS -acall-----VOL (Hu et al. 2025; 2024), which contain hundreds of thousands of QA- pairs, derived from MIMIC-CXL -XX. These datasets rely templates but use radiology reports their original
Table 1: Medical VQA dataset compari- son. Our dataset is the largest to date and provides localized and tagged answers.
aQA
Tugs
CXR-QBA (Ours)
42.2M
detalled
L. fine tuning grade
7.5M
VQA-RAD
3.5K
X
X
brief
SLAKE
14K
brief
ImageCLEF
15K
brief
NQC-
227K
brief
MIMIC CXR VQA
377K
brief
Medical-CXR-VQA
780K
brief
CheXinstruct
brief
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207 tree-dect radiology reports
Extract observations
Extract
Saleitora
tuniingnngg Grado 4,44 sludica 7.33 QA
2
Leau
Build graphs
Exeeute Generation Strategies
Quality
MIMIC-CXR
Faseerecc Setustirn
Pre- Training Grade
81s patwerte
Localiza regions
Limme
a 233
Derive 257 regions
Segment regions
377,110 chest K-ray images
Scene graph construction
DA Generation
Quality Assessment
Figure 2: Overview of our dataset construction pipeline. First, we construct scene graphs based on information extracted from the radiology reports and regions localized in the images. Next, generate question-answw -nnswrr pairs based on templates and the scene graphs. Finally, we automatically assess the quality of generated QA-pairs.
information source, where MIMIC-EXI-MIMILS CECCC-CC---OOOE VQA leverages Chest ImaGenome's (Wu et al. 2021a) scene graphs and Medical-CXR- VQA employs an --ssed extraction strategy similar to ours but without semantic entity mapping, localization, and extraction of textual descriptions. The largest chest X-ray VQA dataset to date, CheXinstruct (Chen et al. 2024). contains 8.5M QA-pairs with images from multiple data sources. However, compared to our dataset, its questions and answers are less diverse, being purely template-bar based and derived from dataset annotations instead of being directly conditioned on the reports. Additionally, none of the described datasets provide the level of detail and annotation richness found in our dataset, which includes bounding boxes, tags, and more detailed, multi-part answers that mirror radiology report sentences.
Grounded Report Generation While localization is not yet common for medical VQA tasks, grounded report generation, i.e. predicting radiology reports with bounding boxes gaining popularity, Notable examples include MAIRA-2 (Bannur et al. 2024). trained on reports manually annotated with bounding boxes and MedTrinity-2255
(Xie et al. 2025), large-scale public dataset with automatically generated reports with bounding boxes. ChEX (Müller et al. (2025) is another model producing textual answers with bounding boxes but does not support VQA tasks.
Scene Graph Construction for Chest X-Rays During VQA dataset construction, auto- matically derive scene graphs from radiology reports. A similar approach is employed by Chest ImaGenome (Wu et al. 2021a b). which uses rule-based information extraction, and RadGraph (Jain) et al. 2021). which uses a relation extraction model. We do not rely on any of those scene graphs but instead leverage LLM-based extraction with semantic entity mapping extract our own more comprehensive and reliable scene graphs. Notably, our method defines a larger set of (localized) regions (257) and findings (221) compared to Chest ImaGenome (29 regions, 53 findings), while additionally rewriting report sentences to focus on specific aspects related to individual graph nodes.
3 THE CXR-QBA DATASET
We present our dataset CXR- QBA, a large-scale chest X-ray (CXR) VQA dataset derived from MIMIC- CXR (Johnson al.) 2024b 2019a), consisting of more than 42M QA-pairs. As shown in Fig. D each QA sample (for given chest X-ray) consists of question (Q), a bounding box (B) supplemented answer (A), and additional tags (regions, findings, certainties, and more). To build our dataset, we propose an automatic pipeline highlighted in Fig. More specifically, we first construct (visually grounded) scene graphs based on the MIMIC-CXR radiology reports using LLM-bbsed information extraction, semantic concept mapping, and localization models (Sec. (.I.. These scene graphs provide a structured description of the study, including sentences (derived from the report) for individual observations. They serve data source for our question-answe template-best based answers and answers derived from the rewritten report sentences (Sec. 3.2). Finally, automatically assess the quality of question-answed pairs using M-M--aseed evaluations (Sec. 3.3
generation, where utilize both
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3.1 SCENE GRAPH CONSTRUCTION
Given a MIMIC-CXR study with a radiology report and accompanying CXRs, we construct a scene graph (Sec. ( consisting of sentence nodes, observation nodes, region nodes, an indication node, and edges between them. Sentence nodes are directly extracted from the reports, containing the raw sentences and their identified section names. Observation nodes represent individual aspects described in the report's 's F PINDINGS or IMPRESSION SION section, containing (i) a textual description, (ii) bounding boxes for associated CXR images and (iii) additional tags, such as positivity, certainty, laterality, regions, and finding classes. Region nodes are created for mentioned anatomical structures and key regions. The indication node contains information from the INDICATION section, including a textual description and an individual observation node, derived from the FIND INGS and IMP IMPRESSII IO sections, that can act as an answer to the indication. We construct these scene graphs in three steps: (a) region localization, (b) information extraction and (c) building the graphs using entity mapping. We refer to Sec. for details.
Region Localization The bounding boxes in our scene graphs (and the derived QA-pairs) are based on fine-grained anatomical structures, allowing us to localize associated findings very precisely. We use the CXAS (Seibold et al. 2022 2023) model to predict segmentation masks of 158 anatomical structures on the 377 110 CXRs from MIMIC-CXR-JFP (Johnson et al.] [202a]] 2019b). Additionally, use the bounding boxes provided by the Chest ImaGenome (Wu al. [02 la bb dataset, which are provided for 29 anatomical structures in most frontal images of MIMIC CXR. Next, we derive a total of 257 localized anatomical structures based on combinations (e.g. intersections, unions, super bounding boxes, etc.) of the available masks and bounding boxes. Finally, we discard any masks or boxes that are too small and derive bounding boxes from the segmentation masks. Note that we define 53 further regions/structures that are either non-localized (e.g. interstitial) or for which we do not have bounding boxes, leading to total of 310 structures/regions.
Information Extraction We use the 227 827 free-text radiology reports provided by MIMIC-CXL as the main source of information for our scene graphs. Using the Llama 3.1 70B (Grattation et al. 2024) model with few-shot prompting, we extract the relevant information (tags and textual deseriptions) in three steps. First, we extract individual sentences from the reports and detect their sections. Next, we extract information about the IND I CAATION section and detect which F IND INGS or IMPRESSION sentences may provide information related to the indication. Finally, we extract individual observations described in the F INDING/ IMPREESIIE RESSION sentences.
Building Scene Graphs using Entity Mapping Given the extracted information from the reports and the computed bounding boxes, we now construct the final scene graph. Therefore, we first map extracted tags to pre-defined sets of values, our reference definitions. This assures high quality and consistency of the scene graphs and enables mapping of observations to the extracted bounding boxes. The reference definitions are based on tags used in other datasets (including PadChest (Bustos et al., 2020) and Chest ImaGenome (Wu et al. 2021b)) as well as SNOMED-CT (SNOMED International 2023)) ) and have been verified by clinical experts. They include synonym lists, hierarchies, and utilize the BioLORD (Remy et al.) (202)) model sentence transformer and identify the closest matching concept based on their semantic embeddings. Additionally, we try to fill in missing information where possible, such as inferring the region from an identified finding. Finally, we build tree of region nodes (using the reference data) and attach the indication information extracted from the report.
relationships. For more robust mapping,
3.2
QUESTION-ANSWER GENERATION
Based on the information available in the scene graphs, we generate question-ansse answer pairs (Sec. D.2 using a template- based approach co-designed with radiologists. We incorporate the textual descrip- tions from the observation nodes which have been derived directly from the report to provide diverse and fine-grained answers. Each answer may consist of multiple answer parts (as shown in Jnd Sec. AA). each describing an individual aspect of the answer with its own sentence, bounding boxes, and tags. We categorize answer parts into three types: (i) answer,, (ii) details, and (iii) golated-informatte
allowing for controlled answer granularity. Answer parts are generated either from templates using scene graph information or directly from observation nodes (Sec.|E.2) Answer parts may also be structured hierarchically, where we use parent-child edges from the scene graph.
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To generate the question-answe answer pairs, employ different strategies for the four types of questions (shown in Fig. T
Q: Descrbbe abnormal findinga the given study:
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Indication: We use the paraphrased indication as the question and create the answer based on the in- dication node in the scene graph, answering the indi- cation based on information in the F FIN INGS and IMPRESS ION sections.
POSTIN
CORTAN
2. Study abnormality:
We generate study-level ques- tions using 13 different templates, with answer parts (Fig.3) based on (filtered) observation nodes.
The lungs are mildy hyperinfiated.
Gaergrtee
3. Region abnormality: We generate questions about individual regions using different templates, consid- ering any region mentioned and additionally randomly sampling non-mentioned regions for balancing.
SOWwwr SEPUAPT
4. Finding: We generate questions about individual find- ings using 7 different templates, considering any find- ing mentioned and additionally randomly sampling non-mentioned findings for balancing.
Answer with multiple parts for different aspects, each with a sentence, tags, and boxes.
Figure 3:
3.3 QUALITY ASSESSMENT
The dataset construction procedure described so far allows us to automatically generate large amounts of QA-pairs. However, each of the steps, errors may be introduced, affecting the overall quality of the dataset. For example, errors during information extraction could lead to incorrect tags, therefore leading to incorrectly filled answer templates or incorrectly selected observation nodes for answers.
To identify and filter such cases, we employ an automatic quality assessment strategy using an LLM- as-a-judge. More specifically, we use Llama 3.1 (Grattafiori et al. 2024) to rate questions and answers by the following five criteria: entailment (does the answer factually align with the original report?), relevance (is the answer relevant to the question?), completeness (is the answer missing something?), as well as question and answer clarity (is the question/answer clear and grammatically correct?). Additionally, we assess the quality of the used scene graphs by identifying missing information (e.g. missing tags localization) issues during the construction process. Finally, combine these individual assessments into an overall quality rating as one of A++, A+, A, B, C, D, or subsets, one for pre-training and one
rated (Sec. D.3 Based on these ratings, we propose for fine-tuning. We exclude all non-frontal images from these datasets, as the localization quality on these images is comparatively low due limitations in the localization models. All QA-pairs with grade of A or better are labeled as line-tunig (FT) grade, resulting in 7.5M pairs, while samples with grade or better are considered pre- pre-training (PT) grade, resulting in 31.2M pairs.
EVALUATION AND ANALYSIS
4.1 EVALUATION OF THE SCENE GRAPHS
For the evaluation of the components in our pipeline, we refer to their original publications. The CXAS (Seibold et al. 2022 2023), used as our primary region localization model, was evaluated against expert annotations on 60 images from PadChest (Bustos al. 2020). They reported mloU scores of 0.93 (frontal images) and 0.85 (lateral images), compared to an inter-rater agreement of 0.95 and 0.83, respectively. The BioLORD model (Remy al.) 2024), used for entity mapping, was evaluated tasks like clinical semantic textual similarity and clinical named entity linking, achieving miilli-benchmark average scores of 89.0 (Pearson correlation) and 59.2 (Top-1 accuracy), respectively.
To evaluate the full graph construction pipeline, including the LLM- extraction, we evaluate our scene graphs by comparing their tags and bounding boxes to hand-labeled expert annotations on MIMIC-CXR For comparison, we include the scene graphs from Chest ImaGenome (Wu et al. 202 baseline. First, evaluate the plausibility of finding tags by comparing seve
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Table Evaluation of our scene graphs, comparing finding tags (a) and associated bounding boxes (b) to expert annotations on MIMIC-CXR subsets, with 95% confidence intervals (bootstrapping. n 1000). Compared to Chest ImaGenome's 's (Wu et al. (021a) scene graphs, we achieve competitive or superior performance, showing that our construction process yields plausible scene graphs.
(a) Evaluation of finding tags against 13 CheXpert (CXP) classes from the MIMIC- C-R-JPGRPPPPP test set and 25 classes, 13 CXP and 12 long-tail (LT) classes, from the CXR-LT 2024 gold standard dataset (Sec. (C2)) We report the Matthews Correlation Coefficient (MCC) macro-averaged over different finding subsets (CXP-5,
CXP-7, CXP-13, LT) and micro-averaged. Compared to Chest ImaGenome, we produce slightly more accurate tags, performing especially well on long-tail -til classes, highlighting the importance of our fine- ine-grained tags.
MIMIC-CXR-IPO Test [MCC]
CXR-LT 2024 Gold JMCC]
Classes
CXP-5
CXP-7
CXP-13
Miero
CXP-7
CXP-13
LT-only
CXR-LT
Micro
Ours (scene graphs)
0.8
0.81
0.69
0.71
0.65
0.57
0.71
0.64
0.67
[0.77.0.82]
[0.79, 0.4)
[0.07. 0,717
10.9% 0.73]
0.69)
[0.54,06]
[0.07.074]
(0.61 0.66)
[0.65, 0.69)
Chest TmaGenome
0.83)
[0.54, 0.69)
10,65, 0.68)
0.39)
10.55,0.5 066)
10.02, 0.66)
(b) Evaluation of finding bounding boxes against 6 finding classes from MS-CXR and 18 classes from REFLACX (Sec. C33) We report the pixel-level intersection-over Union (IoU). Intersection-over Dever-rreiittio (IoP). and Intersection-over Target (loT), each thresholded at 30%, and micro-ade averr-averaged. Compared to Chest ImaGenome, our bounding boxes are better matching the hand-labelee boxes, especially leading to smaller and more precise hoxes (larger ToP), which we assume is due to our more fine- ine-grained region annotations.
MS-CXR
REFLACX
[loU@)30]
[IPP6 30]
[[oT@30]
[loL]:30]]
[loP@ 30] [lcT@:30]
Ours (scene graphs)
0,51
0.56
0.94
0.45
0.54
0.87
(0.47.0.54)
(0.52,06]
[0.92, 0.96]
10:41.0:47)
[0.53,0.56
0.88)
Chest ImaGienome
0.45
0.48
0.98
0.42
0,46
0.95
[0.42, 0.49)
[0. 0.53]
[0.97, 0.99
[0.4 0.43]
[0:44, 4.7
[0.94, (.966
labels derived from our scene graphs two reference annotation sets: the radiologist annotations in SIMIC-CXR-IT v.2.1.0 (Johnson et al. 2024a) with 13 CheXpert (Irvin et al. 2019) classes and the CXR-LT 2024 (Holste et al. [025] 2024) gold-standard dataset (task test set) with 12 additional rare (long-tail) classes. As shown in Tab. 2a our approach (slightly) outperforms Chest ImaGenome, with strong improvements (20%) on long-tail classes, demonstrating the value of our fine-grained finding tags (221 classes) in capturing nuanced study details. Further results are provided in Sec. C.2
To evaluate the accuracy of finding bounding boxes, we compare them with annotations from MS- CXR (Boecking et al., 2024 2022) (6 classes) and REFLACX (Bigolin Lanfredi et al.) 2021] 2022)
(18 classes). We compute study-level pixel masks for each finding as the union of all bounding boxes from positive observation nodes that contain the specific finding tag. We calculate pixel-level Intersection-over-Uniinn Union (loU),
(IoP), and Intersection-over Target (IoT)) for each finding class, considering only image pairs with positive predictions and targets. Thresholding at 30% IoU/ we micro-average the results, reporting the percentage of accurately localized finding-boxes in Tab. 2b On the IoU metrie, our scene graphs perform slightly better than the ones from Chest ImaGenome. The low IoP values indicate that boxes are often too large, but high loT values suggest that they generally cover the finding boxes well. This discrepancy arises because boxes are derived from anatomical regions mentioned in reports, whereas hand-labeled annotations are more precise. Notably, our approach produces more precise boxes (higher IoP) than Chest ImaGenome, likely due to our large number of fine-grained region annotations (257 region classes).
Our analysis confirms that our scene graphs contain plausible finding tags and bounding boxes, with competitive or better quality than Chest ImaGenome. The bounding box quality, in turn, validates the plausibility of our region tags. Overall, our construction process yields high-quality scene graphs, making them reliable foundation for generating QA samples.
4.2 QUALITY OF THE QA-SAMPLES
We assess the quality of our 42.2M QA pairs using an LLM as----aa au-jdee approach co-designed with trained radiologists (Sec. 3.3 For quality control, conducted human study across the five rating

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Weest aubeatings for samples rated
mitd
Senopraph
Frnday cxteciin
Rzta
Dacriries cctnetinn
Extratee lozn
248%
Leciibrtien
put
Iraimat
Relrecace
Cumplames
Questee saity
dacls
Min
1006
1000
(a) Overall rating (top) and sub-ratings (bot- tom).
(h) Reasons for ratings.
Figure 4: Results of quality assessment (Sec. 3.3 We identified significant amount of fine-tuning grade samples, while even pre-training grade samples provide factually accurate answers, especially having high quality main answers.
(a) Finding subcategorics in indication answers, (b) Finding subcategories in study abnormality answers.
Sejio De
Fild anp 2112
(c) Regions in region abnormality answers.
(d) Findings finding answers.
Figure 5: Distribution of tags (finding subcategories, regions, findings) mentioned in answers of different question types (indication, study abnormality, region abnormality, finding). We show their positive ratios, i.e. how often they mentioned in positive versus in negative answers (left) and plot the number of positive and negative mentions of the most frequent tags (right). These fine-grained tags enable filtering and balancing the dataset
be used additional supervision.
criteria (entailment, relevance, completeness, question clarity, and answer clarity), sampling 100 cases per criterion and rating them with the same information provided to the LLM. We found that most 2% of cases were overrated by the LLM (e.g., assigning fine-tuning instead of pre-training grade), with high overall agreement (Cohen's 's kappa) and slight tendency of the LLM to be more critical than human ratings (Sec. (Sec.C..l) Quality rating results for our dataset are shown Fig, 4a We found that 18.6% were fine-tuning grade, 58.8% were pre-training grade, and 22.6% were marked for exclusion. Notably, 85% of individual main answers were rated A or higher. We also analyzed the main causes of ratings (Fig. 4b) and found that A+ samples were limited by minor incompleteness (minor details missing), A samples by minor entailment aspects (facts not explicitly mentioned in extraction,
the report), while B samples were restricted by issues with completeness, and text clarity. Ratings C were caused by major incompleteness or extraction issues, ratings D by contradicting entailments, while non-rated samples where due to the LLM-judge not producing parsable outputs. Our analysis shows that even pre-training grade samples provide factually accurate answers with minor flaws, making them suitable for pre-training purposes.
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4.3 FINDING- AND DISTRIDDSSIIBUIION IN QA- DA-SAMPLES
Our answers include additional tags for findings (and their categories), regions, and answer positivity (positive negative finding), enabling filtering and balancing for specific applications. For instance, undersampling negative answers can help mitigate model biases towards negative predictions. In Fig. 5 we analyze the distribution of these tags. We observe that indication questions tend to have more positive mentions (Fig. 5a) as there is a specific indication to check for - while study abnormality questions have more negative ones (Fig. (Fig.15))
many samples are negative overall. In region abnormality questions, most regions are mentioned slightly more often with positive than with negative findings (Fig. [cc.. while for finding questions mentions are mostly balanced (Fig. 5d). This shows the success of our balanced region/finding sampling used for these two question types.
4.4 UNIQUENESS OF QA-SAMPLES
To measure the effect of our template-based QA gen-
eration strategy on the duplication of QA pairs, we
Table 3: Uniqueness of QA samples. While our template-bast basee strategy leads to many du- plications in the question texts, our answer texts are highly diverse, while the diverse sets of finding and region tags demonstrate large variability of unique medical conditions.
count the numbers of (i) unique question texts, (ii) unique answer texts, (iii) unique finding tag sets in an- swers, and (iv) unique region tag sets in answers. We compare these to the overall number of QA samples to compute the average duplication factor. Results are shown in Tab. 3 As expected, we have high du- plication rates for question texts except for indication questions, where the questions are based on the indi- cation section of each report, leading many unique questions. Despite the template-basee based questions, we still provide thousands of different individual ques-
# Unique Samples
Average Duplications
Question
177072
Indication
172452
+ Study abnormality
65
183
tions. More notable is the low duplication rate of answer texts, ranging from swer types. This is possible, because answer texts are not solely template-basee based but also include rewrit- ten sentences from the original reports. Overall this demonstrates that we provide large-scale dataset with diverse answers.
+ Region abnormality
3926
5252
6 for different an-
+ Finding
630
14950
Answer Texts
7 760
Finding Tag Sets
764 022
55
Region Tag Sets
586 847
72
4.5 ANSWER CHARACTERISTICS
Our QA-samples provide detailed free-text answers consisting of one or even multiple sentences (i.c. an- swer parts). In Fig. 6 analyze the distribution of lengths of these answers and study differences be- tween types of answers questions. The median answer length is 14 words, with similar lengths for question types except for indication questions, where answers are much longer (46 words). We also observe that related information answers are much longer (22 words) than main answers (9 words) or details answers (7 words), which expected as they can provide lot additional context to the answers. Answers describing positive findings are typically very long (18 words), considerably longer than nega- tive finding answers (10 words). This highlights that dataset provides nuanced finding description in their answers, following the level of detail typically present in radiology reports.
Dhcrall
modian
Quatii
abnommaity reginn absurmaltt
finding
main onrwer denils related Information
finding (puckive) finding (sapuine) devise (puilive)) asquidtion
60
90
100
120
140
100
Number of words per answer (comiitering all sub-atswens)
Figure 6: Distribution of answer lengths. We provide nuanced answers with detailed free- text finding descriptions.
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Table 4: Results on our structured VQA task, evaluated on our fine-tuning grade test set (95% confidence intervals). Our model was trained on 1M or 2M pre-training (PT) grade samples, on 1M fine-tuning (FT) grade samples, or on IM pre-training (PT) followed by 1M fine-tum tueettunng (FT) samples from our dataset. We compare it with MAIRA-2, Qwen3-VL (4b, Instruct), and LLaVA-Med v1.5. Our dataset enables training VLMs to predict correct, visually grounded, and tagged answers.
Ours
MAIRA-2
Qwen3-VL (4B)
LLaVA-Mod v1.5
CXR-QBA Training Set
PT(IM)
PT(2M)
FT(IM)
(P((M)))P((MM)
X
X
X
Logical Prec.
0.67
0.68
0.76
0.78
0.25
0,63
0.47 10.4n, 0.48)
[0,00, 0.68)
[0.67, 0.991
10.75, 0:76]
0.777,07781
10:25, 0.26
10,63, 0.641
RadFaet
Logical Rec.
0.69
0.70
0.75
0.77
0.64
0.58
0.08 [0.07, 0.08)
(0.68. 0.69)
[0.69, 0.70]
[0.74, 0.76)
[0.76,0.77]
[0.65, 0.65)
[0.57.0.59]
Grondiing Prec.
0.87
0,88
0.87
0.89
0.69
0.61
[0.80] 0.87)
10.s7 0.88)
0.58)
0.891
[0.67,0.71]
[0.00, 0,62)
RadStruc VQA
Grounding Rec.
0.92
0.93)
0.92
0.92
10,88, 0.59)
0:90]
10.11.0.12)
0.12
[0.50, (.53)
Finding Prec.
0.67
0.68
0.68
0.70
[0 0.66)
[0.68. 0.668
046)
[0: 00:70,0.71]
Finding Roc.
0.66
0.68
(0/000662]
0,5))
0.67]
0.49)
Tags
Finding-pos Prec.
0.32
0.37
0.4
0.45
[..31,0.34]
[0. 0.58]
[0.40, 0.43]
[0:45, 0.46]
Finding-pos Roc.
0.13
0,16
0.26
0.31
[0.12.0.14]
[0.15, 0.177
1o.25, 0.277
[0.30, 0.333
Region Prec.
0.61
0.61
0.67
0.69
(0,60) 0.61]
[0.6], 0.63
[0.66, 0.68]
[0.69,0 0.70)
Region Roc.
0.62
0,66
[0.61.
[0.62, 0.63]
[0.65, 0,67]
(0,67.0.6)]
5 STRUCTURED VQA TASK
To showcase the utility of our dataset, introduce the Structured Visual Question Answering (structured VQA) task. Given a free-text question, the model is expected to generate a (multi-part free-text answer accompanied by bounding boxes and tags (e.g., findings, regions). Unlike classical VQA, this task demands more structured answers, thus improving interpretability and clinical utility.
Baseline Models We implement our own model based on the Llava architecture (Liu et al. 2023 using Rad-DINO (Pérez-García al.) 2025) for image encoding and the Llama 3.2 3B (Grattafiri eet al. 2024) language model. Our CXR-QBA dataset provides the necessary targets, which we format into sequences using XML-style structures and special tokens to represent tags and bounding boxes. We train this model for one epoch on 1M or 2M pre-training (PT) grade, on IM fine-tuming (FT) grade, or on IM pre-training (PT) followed by 1M fine-tuning (FT) grade QA QA-pairs (MIMIC-CXR train split) and evaluate them on the FT grade test set. For comparison, we include MAIRA-2 (Bannur et al.) 2024). the only publicly available CXR report generation model that explicitly supports bounding box prediction. To partially support the task, we adapt its prompt. Furthermore, we include two other vision-language models, namely Qwen3-VL (4b, Instruct) Bai et al. (025); Team (2025), and VL- v1.5 et al. (202)), both comparable larger in size than where trained, among others, on VQA instructions. While they were not optimized for bounding box prediction, we still prompt them to provide textual bounding boxes in their answers. They do, however, not support tag prediction. Note that none of the three comparison models was fine-tuned on our task. Additional ablations provided in Sec. F.T and further details in Sec. F3
model. These models
RadStrucVQA Metric For evaluation, we introduce the RadSirucVQL. VQA metric, which closely follows the RadFact (Bannur et al., 2024) metric introduced for radiology report generation but is generalized to structured VQA. Like RadFact, we identify whether individual predicted answer parts are entailed with target answer parts and vice-versa, in our case using Llama 3.1 8B. For entailed pairs, we compute whether they are visually grounded, i.e. whether their bounding boxes are precise enough considering their references, and whether finding and region tags are correctly reported. This is conducted bi-directionally, using either the targets as references for the predictions or vice-versa, resulting in precision or recall scores, respectively. To verify the reliability of the Llama 3.1 8B model for entailment prediction in our metric, we compared it with Llama 3.1 70B and Qwen3-32B. For all sub-metrics. larger models (which almost perfectly agree with each other). More details on the metric and this assessment can be found in Sec. F2
found high correlations (0.88 to 0.97) between the small Llama 3.1 8B and the

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Results Results are shown in Tab. Our models achieve high scores in both textual content (logical) and grounding metrics. On most metrics, models trained on the FT grade set outperform models trained only on the PT grade set. Training first on 1M PT samples followed by training 1M FT samples achieves the best performance, even better than training on 2M PT samples, showing that even our PT grade dataset provides high-quality targets. All of our models outperform the baselines not trained on this task. MAIRA-2 still achieves 75% of our best model's logical recall, suggesting it captures most relevant information while not contradicting with our answers. However it predicts additional findings (lower precision), probably because it was trained for reporting and cannot react well to questions. MAIRA-2's 's grounding precision significantly exceeded its recall, because it was trained to predict bounding boxes only for positive findings. Qwen3-VL outperforms MAIRA-2 on the precision scores indicating that it can better react to questions. Notably, is almost on-par or outperforms MAIRA-2 in grounding quality. V-a v1.5 does not perform well and did not provide bounding boxes (despite being instructed to do so). We assume that this is because that model typically requires fine-tuning on specific tasks and it does not perform well in zero-shot settings. Also, it did not provide individual answers per finding, explaining its low recall. Our models successfully predict finding and region tags in most cases. However, performance drops when focusing solely on positive findings (f inninnnnggii pod)nggposs,.. indicating potential underprediction due to our training procedure or limitations in the pre-trained components. While further analysis would be required, such problems may also lead to flaws in textual answers. Importantly, our dataset's 's detailed tags enable fine- graiegg analysis of such issues while also enabling potential solutions like data filtering or balancing, making it well-suited for complex training scenarios.
DISCUSSION
6.1
USE CASES AND IMPACT
We introduced the CXR-QBA QBA dataset, a large-ccale, automatically constructed VQA dataset for CXRs offering structured, tagged, and localized answers. By design, it is particularly well-suited for the structured VQA task proposed in Sec. Additionally, its large size and detailed answers make it a valuable resource for pre-training vision-language models. The accompanying tags further enable filtering and balancing of the dataset to suite specific needs. Furthermore, our fine-grained scene graphs with bounding boxes, textual descriptions, and tags can serve versatile data source for various purposes. For instance, they can be leveraged to create customized datasets for grounded report generation VQA, even as direct training source for graph generation models predict scene graphs on unseen chest X-rays, enabling the creation of even larger datasets. Moreover, the bounding boxes and tags provided with the scene graphs can be used for longitudinal analysis, including region-level examination. They can also be used to train models for pathology localization or classification, providing fine-grained and long-tail diagnosis targets that are often lacking in existing datasets. Finally, we release all source code for dataset construction, enabling future work to transfer our dataset construction framework to other modalities beyond chest X-rays.
6.2 LIMITATIONS
Our dataset was automatically constructed, relying on models and templates instead of human annotations. While this enables the generation of a large number of QA-pairs, it may also introduce potential errors and biases like LLM hallucinations or template priors. We apply automatic quality assessments and extensive quality controls mitigate these risks, but even those have their limitations and some are LLM-based. Therefore, users should be aware that inaccuracies may remain and exercise caution in critical applications. We strongly advise against using this dataset as the sole source for fine-tuning or evaluating models in clinical practice. For such purposes, we recommend using small-scale, gold-standard (fully human- annottaedd) datasets, which are, however, beyond the scope of this work. Furthermore, our template-basee approach may also limit diversity and introduce grammatical errors, though we partially mitigate these issues by incorporating answers from actual report sentences and through quality assessment. Additionally, the dataset is derived from a single source dataset, inheriting demographic and clinical biases present in this specific patient population. This may limit the generalizability of models trained on this dataset. Finally. it focuses on chest X-ray studies from a single dataset, excluding longitudinal and differential questions as well as other imaging modalities. Future work could extend our approach broader question types and modalities.
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ETHICS STATEMENT
This work does not directly involve human subjects and does not collect new human subject data. All human subject data used (such as chest X-rays or radiology reports) are derived from existing public datasets, which were collected independently of and prior this work. We will release the dataset as a credentialed dataset on the Physionet platform (Goldberger et al.] 2000). This ensures compliance with the license of MIMIC-CRR [Johnson al. 2024b), from which our dataset is derived. also serves to protect the included data, which is indirectly derived from patient health information. While enabling researcher access, it restricts use for other purposes and requires completion of privacy and ethics course. Furthermore, protect privacy and comply with the MIMIC-CRR license, all data processing was performed locally, and no data were sent to external services.
REPRODUCIRILITY STATEMENT
We provide full details to ensure reproducibility of our work.
The construction of the dataset is The structured VQA task and associ- ated metrics are presented in Sec. 5 with further details in Sec. All source code for dataset construc-
described in Sec. 3 with additional information in Secs. D and
tion and the structured VQA task available at httttp::: //gg t.ub. com/ / phi pinilipaa mppmue/ er mimic- ext- CXx / The constructed dataset is available through the PhysioNet platform (Goldberger et al. 2000) at httpps: http///hssionnooo org hwtp://onttt/////eee prontenn/miiman /ttm/mmiiiiiii ext exr /bba/ 0/ is accessible to researchers after credentialing by PhysioNet (which requires a short online training). This hosting is necessary because the dataset is derived from MIMIC-CXR.
ACKNOWLEDGMENTS
This work was partially funded by ERC Grant Deep4MI (Grant No. 884622).
REFERENCES
Seongsu Bae, Daeun Kyung, Jaehee Ryu, Eunbyeol Cho, Gyubok Lee, Sunjun Kweon, Jungwoo Oh, Lei JI, Eric Chang, Tackeun Kim, and Edward Choi. MIMMIC-EX-MMMICCCXXO VQA: A Complex, Diverse, And Large-Scale Visual Question Answering Dataset for Chest X-ray Images, 2024. URL
tttpss://pyyiioet.orrg httg://onteett/mmm mwc-ext mimi c- cxr vga 0.00/
Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, Humen Zhong, Yuanzhi Zhu, Mingkun Yang, Zhaohai Li, Jianqiang Wan, Pengfei Wang, Wei Ding. Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang. Tianbao Xie, Zesen Cheng, Hang Zhang, Zhibo Yang. Haiyang Xu, and Junyang Lin. Qwen2.5-vl technical report. arXiv preprint ar arXiv:2502. / 13923, 2025.
Shruthi Bannur, Kenza Bouzid, Daniel C. Castro, Anton Schwaighofer, Anja Thieme, Sam Bond- Taylor, Maximilian Ilse. Feraando Pérez-Gareía, Valentina Salvatelli, Harshita Sharma, Felix Meissen, Mercy Ranjit, Shaury Srivastav, Julia Gong, Noel C. F. Codella, Fabian Falck, Ozan Oktay, Matthew P. Lungren, Maria Teodora Wetscherek, Javier Vlvarez-Valle, and Stephanie L.
Hyland. MAIRA-2: Grounded Radiology Report Generation, September 2024. URL http:
wxxxiv.erg/a hb.gg//bb/2440 04449 arXiv:2406.0444 04449 [cs].
Asma Ben Abacha, Sadid A. Hasan, Vivek V. Datla, Dina Demner-Fustiee Fushman, and Henning Müller. VQA-Med: overview of the medical visual question answering task at ImageCLEF 2019. Proceed- ings of CLEF (Conference and Labs of the Evaluation Forum) 2019 Working Notes, September 2019. Meeting Name: CLEF (Conference and Labs of the Evaluation Forum) 2019 Working Num
Pages: 11 Place: Lugano, Switzerland Publisher: 9-12 September 2019.
Ricardo Bigolin Lanfredi, Mingyuan Zhang, William Auffermann, Jessica Chan, Phuong-Ant Duong, Vivek Srikumar, Trafton Drew, Joyce Schroeder, and Tolga Tasdizen. REFLACX: Reports and eye-tracking data for localization of abnormalities chest x-rays, 2021. URL https
//physionet.co org/ wwwp///ttgg/////...............t content/t////ccocooo temeeett/refaace xray- wwcyy-ooaaiiiann wwtccaaiiaaiion///oo 1.0.0/
Ricardo Bigolin Lanfredi, Mingyuan Zhang, William F. Auffermann, Jessica Chan, Phuong-Ani T.
Duong, Vivek Srikumar, Trafton Drew, Joyce D. Schroeder, and Tolga Tasdizen. REFLACX. dataset of reports and eye-tracking data for localization of abnormalities in chest x-rays. Sci
11

---

Published as a conference paper at ICLR 2026
Data, 9(1:3350, June 2022. ISSN 2052-4463. doi: 10.0038441177-02--141411 URL https:
www. nature.
Benedikt Boecking, Naoto Usuyama, Shruthi Bannur, Daniel C. Castro, Anton Schwaighofer, Stephanie Hyland, Maria Wetscherek, Tristan Naumann, Aditya Nori, Javier Alvarez- Valle, Hoifung Poon, and Ozan Oktay. Making the Most of Text Semantics to Improve Biomedi- cal Vision-Language Processing. In Shai Avidan, Gabriel Brostow, Moustapha Cissé, Gio- vanni Maria Farinella, and Tal Hassmer (eds.), Computer Vision ECCV 2022, volume 13696, pp. 1-21. Springer Nature Switzerland, Cham, 2022. doi: 10.1007/978 --00--009--3----0---3-3 Series Ti-
URL htRps:tss:/ //tinkk//iik... www.inger.comm/....... 1007100...00---111
30tp:/nng..ccomm..11110/773333-0 031--20059--
tle: Lecture Notes in Computer Science.
Benedikt Boecking, Naoto Usuyama, Shruthi Bannur, Daniel Coelho de Castro, Anton Schwaighofer, Stephanie Hyland, Harshita Sharma, Maria Teodora Wetscherek, Tristan Naumann, Aditya Nori, Javier Alvarez Valle, Hoifung Poon, and Ozan Oktay. MS-CXR: Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing, 2024. URL https
Aurelia Bustos, Antonio Pertusa, Jose-Maria Salinas, and Maria de la Aidesia-Vayá PadChest: A large chest x-ray image dataset with multi-label annotated reports. Medical Image Analysis, 66: 101797, December 2020. ISSN 1361-8415. doi: 10. (0.10166j.mediaa20000 2020. 201.:02000.1111111 101797. URL https
www.wsciencd..eec..ocm com/ httpa///............../////////.
Zhihong Chen, Maya Varma, Justin Xu, Magdalini Paschali, Dave Van Veen, Andrew Johnston, Alaa Youssef, Louis Blankemeier, Christian Bluethgen, Stephan Altmayer, Jeya Maria Jose Valanarasu, Mohamed Siddig Eltayeb Muneer, Eduardo Pontes Reis, Joseph Paul Cohen, Cameron Olsen, Tanishq Mathew Abraham, Emily B. Tsai, Christopher F. Beaulicu, Jenia Jitsev, Sergios Gatidis, Jean-Benoit Delbrouck, Akshay S. Chaudhari, and Curtis Langlotz. A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretation, December 2024. URL http: org/ abss/4000 12208 arXiv:2401. 12208 [cs].
Ary L. Goldberger, Luis A. N. Amaral, Leon Glass, Jeffrey M. Hausdorff, Plamen Ch. Ivanov, Roger G. Mark, Joseph E. Mietus, George B. Moody, Chung- Kang Peng. and H. Eugene Stanley. PhysioBank, Toolit,, and PhysioNet. Circulation, 101(23)ee15--2222 June 2000. doi: URL lttps:://www aha www.rnaass.org/oo/fflllll
00.1161/01.CIR.01.222 e11..
1161/01 101 23. e215 Publisher: American Heart Association.
Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, et al. The Llama Herd of Models, November 2024. PRLLattt:///xxx...rr//bb//////// utXiv:2407.21788 [es].
Gregory Holste, Yiliang Zhou, Song Wang, Ajay Jaiswal. Mingquan Lin, Sherry Zhuge, Yuzhe Yang. Dongkyun Kim, Trong-Hieu Nguyen-Mau, Minh-Triet Tran, Jaehyup Jeong, Wongi Park, Jongbin Ryu, Feng Hong, Arsh Verma, Yosuke Yamagishi, Changhyun Kim, Hyeryeong Seo, Myungjoo Kang, Leo Anthony Celi, Zhiyong Lu, Ronald M. Summers, George Shih, Zhangyang Wang, and Yifan Peng. Towards long-tailed, multi-label disease classification from chest X- ray: Overview of the CXR-LT challenge. Medical Image Analysis, 97:103224, October 2024. ISSN 1361-8415. doi: 10.10166j.meeia.200000 103224. c0m/ Stano//ccciceeerrrrccee///////B. 94445611118444440444
/wwww sciencedirect
Gregory Holste, Mingquan Lin, Song Wang, Yiliang Zhou, Yishu Wei, Hao Chen, Atlas Wang, and Yifan Peng. CXR-LT: Multi-Label Long-Tailed Classification on Chest X-Rays, 2025. URL
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. LoRA: Low-Rank Adaptation of Large Language Models. October 2021. URL
/ettps:/oopenree
net wtt/feouumeeeee wtworrrmmmiinneeeeeeee..
Xinyue Hu, Lin Gu, Kazuma Kobayashi. Liangchen Liu, Mengliang Zhang, Tatsuya Harada, Ronald M. Summers, and Yingying Zhu. Interpretable medical image Visual Question Answering via multi-modal relationship graph learning. Medical Image Analysis, 103279,,, October 2024. ISSN 1361-8415, doi: 10. ccmm//cieence/attiicle///////pp
103279. URL https: www.wweeiiinnnccetttt.t...... 0400844455440000
12

---

Published as a conference paper at ICLR 2026
Xinyue Hu, Lin Gu, Kazuma Kobayashi, liangchen liu, Mengliang Zhang, Tatsuya Harada, Ronald Summers, and Yingying Zhu. Medical-CXR VQA dataset: A Large- Scale LLM-Enhanced
Medical Dataset for Visual Question Answering Chest X-Ray Images, 2025. URL https:
//physionets org wwwooetttrr/oooooooooeoeooo www.rr/w//./////..oo.oooo..ooo// http///oneetteeeecaaaaca /medi -or dateeedatttttttile 0.0
Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ileus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, Katie Shpanskaya, Jayne Seekins, David A. Mong, Safwan S. Halabi, Jesse K. Sandberg, Ricky Jones, David B. Larson, Curtis P. Langlotz, Bhavik N. Patel, Matthew P. Lungren, and Andrew Y. Ng. CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01): 597159999959995000 July 2019. ISSN 2374-3468,
doi: 10. v33001.333300 133001..30111011111101 133301590. aaai org/ htt///iddxxx./ wwpppnappxppppp....a./apapapaaae
URL http:///.aa.aaaaoooooa//aa/aaaaa htt.dep..pp/...a/aattttca////eee 3834 01.
Number:
Saahil Jain, Ashwin Agrawal, Adriel Saporta, Steven Truong, Du Nguyen Duong N. Duong, Tan Bui, Pierre Chambon, Yuhao Zhang, Matthew Lungren, Andrew Ng, Curtis Langlotz, and Pranav Rajpurkar. RadGraph: Extracting Clinical Entities and Relations from Radiology Reports. Proceedings of the Neural Information Pro- cessing Systems Track Datasets and Benchmarks, December 2021. URL
Alistair Johnson, Matthew Lungren, Yifan Peng, Zhiyong Lu, Roger Mark, Seth Berkowitz, and
Steven Horng. MIMIC-CXR-JPG- chest radiographs with structured labels, 2024a. URI. https
Alistair Johnson, Tom Pollard, Roger Mark, Seth Berkowitz, and Steven Horng. MIMIC-CXR Database, 2024b. URUsstees//llysiioet..ooo//////
/ww.ooontttttttiiiimoomoemommtet /mimic cxr/2.1.07
Alistair E. W. Johnson, Tom J. Pollard, Seth J. Berkowitz, Nathaniel R. Greenbaum, Matthew P. Lungren, thih-ying Deng, Roger G. Mark, and Steven Horng. MIMIC-CXR, a de-identified pub- licly available database of chest radiographs with free-text reports. Sci Data, 6(1):317, December 2019a. ISSN 2052-4463 doi: 10. 10730//41197-001-022222 com/ htmm/artiicee//s4115/11 000-09277-99--2--0--0
URL httpstt::: www nature
Alistair F. W. Johnson, Tom J. Pollard, Nathaniel R. Greenbaum, Matthew P. Lungren, Chih-ying Deng, Yifan Peng, Zhiyong Lu, Roger G. Mark, Seth J. Berkowitz, and Steven Horng. MIMIC-
CXR-JPG, large publicly available database of labeled chest radiographs, November 2019b. URL
org/ www./s////. 07042 arXiv: 1901.0090 [cs].
Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph Gonzalez, Hao Zhang, and lon Stoica. Efficient Memory Management for Large Language Model Serving with PagedAttention.. In Proceedings of the 29th Symposium on Operating Sys- tems Principles, SOSP '23, pp. 61 1-626, New York, NY, USA, October 2023. Association for Computing Machinery. ISBN 479-884007--022--11 doi: 10. 0..114//3600006361111666 URL
https://dl.acm.co
htt/////.0..111110300000000000.. 361166
Jason J. Lau, Soumya Gayen, Asma Ben Abacha, and Dina Demner-Fushman, A dataset of clinically generated visual questions and answers about radiology images. Sci Data, 5(1):18025,, November 2018. ISSN 2052-4462 doi: 10. 10.1038/sdata.2018.25
URLbttpa www. nat com/
aaticles/sddta2211
Chunyuan Li, Cliff Wong, Sheng Zhang, Naoto Usuyama, Haotian Liu, Jianwei Yang, Tristan Naumann, Hoifung Poon, and Jianfeng Gao. LLaVA-Med: Training Large Language-ant Visionsoon Assistant for Biomedicine in One Day. In NIPS '23: Proceedings of the 37th International Conference on Neural Information Processing Systems, 2023.
Lin, Jiaming Tang, Haotian Tang, Shang Yang, Wei-Ming Chen, Wei-Chen Wang, Guangxuan Xiao, Xingyu Dang, Chuang Gan, and Song Han. AWQ: Activation-aware Weight Quantization for On-Device LLM Compression and Acceleration. Proceedings of Machine Learning and Systems, 87-100, May 2024. URL /tLLattoss//pppceee........ www.s..smss.c...........ms..... org paper
html
13

---

Published as a conference paper at ICLR 2026
Bo Liu, Li-Ming Zhan, Li Xu, Lin Ma, Yan Yang. and Xiao-Ming Wu. Slake: A Semantically- Labeled Knowledge-Enhanced Enhancen Dataset For Medical Visual Question Answering. In 2021 IEEE 18th International Symposium Biomedical Imaging (ISBI), pp. 1650- April 2021. doi: 10. 10991911492111111 1.2021. .9330100. URL/tttps: /ttttp:://eeeexllree.... eee. wtw.:///.eeeee.rrratrbbb.raa (eccument/943400 ISSN: 1945-8452.
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jac Lee. Visual Instruction Tun- ing. Advances in Neural Information Processing Systems, 36:34892-34916, Decem-
ber 2023. URL https:://paaaeee nips. wwt.iips.cppppper.riipp htt.ss//pperrr..ppp.ccppppprrrrr 6dcf
Philip Müller, Georgios Kaissis, and Daniel Rueckert. ChEX: Interactive Localization and Region Description in Chest X-Rays. In Aleš Leonardis, Elisa Ricci, Stefan Roth, Olga Russakovsky, Torsten Sattler, and Gül Varol (eds.), Computer Vision ECCV 2024, pp. 92-111, Cham, 2025. Springer Nature Switzerland. ISBN 978-3-031-72664-44 doi: 10. 00177977-3-031-72665-
6.
Fernando Pérez-García, Harshita Sharma, Sam Bond-Taylor, Kenza Bouzid. Valentina Salvatelli, Maximilian Ilse, Shruthi Bannur, Daniel C. Castro, Anton Schwaighofer, Matthew P. Lungren, Maria Teodora Wetscherek, Noel Codella, Stephanie L. Hyland, Javier Alvarez- Valle, and Ozan Oktay. Exploring scalable medical image encoders beyond text supervision. Nat Mach Intell, 7(1):119-130, January 2025. ISSN 2522-5839.
URL doi: 10. 1011103844225----00006-
hittps://et http://wwwwatttue..c.. wataatrre..m/aatiieeeeesseseeeee 4220://2222252---22-2022--
Publisher: Nature
Publishing Group.
Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton, and Christopher D. Manning. Stanza: A Python Natural Language Processing Toolkit for Many Human Languages. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 2020.
gtip:///22200sttaa....mm http:///ww....ppoooooo//o//aaaaa URL https: /pppppp...m...m sta.pssstafrreee.. edw/pubs
pdf
François Remy, Kris Demuynck, and Thomas Demeester. BioLORD-2000 semantic textual repre- sentations fusing large language models and clinical knowledge graph insights. Journal of the American Medical Informatics Association, 31 1845-185, September 2024. ISSN 1527-974X.
doi: 10. 10.1099/ammia//cae022, URL (ttlhttss: //oi org/ /9t//9//.mm..m.... 029
Constantin Seibold, Simon ReiB, Saquib Sarfraz, Matthias A. Fink, Victoria Mayer, Jan Sellner, Moon Sung Kim, Klaus H. Maier-Hein Jens Kleesiek, and Rainer Stiefelhagen. Detailed Anno- tations of Chest X-Rays via CT Projection for Report Understanding. arXiv, October 2022. doi: 10-48550/arXxv22210.0441 URL htlth:tp:// pestps://bmvcaee http://bwcc222...2 mpi - inf www..mpg.aco de/ pdf arXiv:2210.03416 [es].
Constantin Seibold, Alexander Jaus, Matthias A. Fink, Moon Kim, Simon ReiB, Ken Hermmann Jens Kleesiek, and Rainer Stiefelhagen. Accurate Fine-Grained Segmentation Human Anatomy in Radiographs via Volumetric Pseudo-Labeling, June 2023, HtRL:tt:://// org. abbs/ 2336. 03934 arXiv:2306.0399 [eess].
SNOMED International. SNOMED CT, 2023. URL htttp:://ww..snmmmed org
Qwen Team. Qwen3 technical report, 2025. URL
Tao Tu, Shekoofeh Azizi, Danny Driess, Mike Schaekermann, Mohamed Amin, Pi-Chuan Chang, Andrew Carroll, Charles Lau, Ryutaro Tanno, Ira Ktena, Anil Palepu, Basil Mustafa, Aakanksha Chowdhery, Yun Liu, Simon Kornblith, David Fleet, Philip Mansfield, Sushant Prakash, Renee Wong, Sunny Virmani, Christopher Semturs, S. Sara Mahdavi, Bradley Green, Ewa Dominowska, Blaise Aguera y Arcas, Joelle Barral, Dale Webster, Greg S. Corrado, Yossi Matias, Karan Singhal, Pete Florence, Alan Karthikesalingam, and Vivek Natarajan. Towards Generalist Biomedical AI. NEJM AI, 13)::loa22000138, February 2024. doi: 10. 10106/Aooo2300013 URL Intps 2300138
7/ai www.nnejjmmmmm www..//.mo.........o...
Publisher: Massachusetts Medical
Society.
Joy Wu, Nkechinyere Agu, Ismini Lourentzou, Arjun Sharma, Joseph Paguio, Jasper Seth Yao, Ed- ward Christopher Dee, William Mitchell. Satyananda Kashyap, Andrea Giovannini, Leo Anthony Celi, Tanveer Maheood, and Mehdi Moradi. Chest ImaGenome Dataset, 2021a. URL
https://physiooett. http::///ooossooc..ccccc//rerrts wtt.:ssteemmmmmmmmmmeeeeemmm 0..0
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Joy T. Wu, Nkechinyere N. Agu, Ismini Lourentzou, Arjum Sharma, Joseph A. Paguio, Jasper S. Yao, Edward C. Dee, William Mitchell, Satyananda Kashyap. Andrea Giovannini, Leo A. Celi, and Mehdi Moradi. Chest ImaGenome Dataset for Clinical Reasoning, July 2021b. httht
//axxiv.or org/ abs www.aaaaaa... 08. 00316 6
arXiv:2108.00311 [cs].
Yunfei Xie, Ce Zhou, Lang Gao, Juncheng Wu, Xianhang Li, Hong- Yu Zhou, Sheng Liu, Lei Xing, James Zou, Cihang Xie, and Yuyin Zhou. MedTrinity-25M. 255M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine, March 2025. URL http: /ttp:v/aaxivvvv..a wwwpabsorg//bs/ arXiv:2408. 02200) [es].
2408. 02900
Xiaoman Zhang, Chaoyi Wu, Ziheng Zhao, Weixiong Lin, Ya Zhang, Yanfeng Wang, and Weidi Xie. Development of a large-scale medical visual question-amswering dataset. Commun Med, (1):1-13, December 2024. ISSN 2730-664X, doi: 00000304443330002-00700000 URL BRLhttpa es 02 024-0009-2220000--
www.. waturattueacmmmmmmmm com a rt
Publisher: Nature Publishing
Group.
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A EXAMPLE QA-PAIRS FROM OUR DATASET
(a) Negative.
(b) Positive.
Figure 7: Examples of indication questions. Questions based the paraphrased IND T CATTONN section while each main answer is generated based on the indication node from the scene graph.
Desurbaay
Ballang
(a) Abnormality descriptions Istudy-level or category-
Or someiien to Perert
Cleck prosere of lnglenn
Clock groseee Imglerds
displrnges
Sarthorr
There harture prane Paanet splinn
Trapacenator
(b) Abnormality assessment (catcgory-level).
(c) Device descriptions.
Figure 8: Examples of study abnormality questions. Questions are based on one 13 templates. Answers may consist of several answer parts, where each describes an individual aspect (about the overall study or finding category). Individual answer parts are constructed based on observation nodes, filtered based on finding categories relevant to the question, where individual answer parts may be organized bierarchically (indicated by indentations) based on parent-child edges in the scene graph. Additionally assessment answers (b) start with template-based yes/no answer.
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Sear Wet Rimnon
Dearrba ohb.
Ewirbseten Eebrdme Eetees White
Deseriete lewer lote
laggo
(a) Region description.
atnomea Indias
rudic
(b) Region assessment.
(c) Region devices.
Figure 9: Examples of region abnormality questions. Questions are based on one of 6 templates. Answers may consist of several answer parts, where each describes an individual aspect (about the region). Individual answer parts are constructed based on observation nodes relevant to the region, where individual answer parts may organized hierarchically (indicated by indentations) based parent-child edges in the scene graph. Additionally assessment answers (b) start with template-based yes/no answer. Some templates also ask specifically about devices in the region (c).
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Spec
Toun pliaary
(a) Finding assessment.
(h) Finding description.
Inctad entrer
Thi Iccrad ban.
Rarge
eugating for diatrod
(c) Finding location.
(d) Device.
Figure 10: Examples of finding questions. Questions are based on one 7 templates. Answers start with template-based answer part to identify the finding presence (a), provide severity summary device (d). Additional details may be provided in
(b), describe the location (c), or presence answer parts based observation nodes relevant to the finding, where individual answer parts may be organized hierarchically (indicated by indentations) based on parent-child edges in the scene graph.
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B FURTHER ANALYSIS OF QA- QA-SAMPLES
Welhhn deeerio
ADCESSTTO
bake cocoommnii
(a) Finding jeateoreate for different question types (indication, study abnormality, region abnormal- ity, finding).
(b) Finding sub-categories (all samples).
(c) Regions (all samples).
(d) Findings (all samples).
Figure 11: Tags (finding main- and cub-categ,,i,s, regions, findings) mentioned in answers. We show their positive ratios (top/lfft), i.e. how often they mentioned in positive versus in negative answer parts and plot the number of positive and negative mentions of the most frequent tags (bottom/rrihh)..
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C EVALUATION DETAILS
C.I QA EVALUATION: ASSESSMENT OF THE LLM-AS-A-JUDGE
Table Systematic study to reliably quantify the effectiveness of the LLM-aa-a-judge. For each of the five quality criteria (entailment, relevance, completeness, question clarity, answer clarity), we independently sampled 100 cases (without replacement). Each of these cases was rated by a human annotator provided with the same information available to the L.LM-as-a-judge. We show two metrics: (i) the percentage of cases where LLM judgment lead to an overrated grade (e.g. fine-tuning instead of pre-training grade) compared to human judgment and (ii) Cohen's 's kappa, standard metric for inter-rater reliability. In a maximum of 2% of the cases the LLM judgment lead to an overrated grade while we found overall high agreement with a tendency of the LLM to be more critical than human ratings (especially for completeness and clarity).
Metric
Entailment
Relevance
Completeness
Question clarity
Answer clarity
(7 classes)
classes)
(5 classes)
(6 classes)
15 classes)
1 overrated grade
1%
2%
0%
Cohen's kappa
0.49
0.41
0.32
0.32
3//
351
25
+
VNL
ON
Ellandd 78b
Figure 12: Confusion matrix comparing the assigned quality ratings between using Llama3. 8b (default) and Llama3.1 70b an LLM--juge (see Secs. 3.3 and 4.2). In most cases, ratings differ only slightly. Most importantly, low-quality samples (as rated by Llama3. 70b) are almost never assigned to fine-tuning grades (A or higher) by Llama3. 1 8b. We thus decided to use Llama3.1 8b as our default rater, as it is much more computationally efficient.
C.2 SCENE GRAPH EVALUATION: FINDING TAGS
Table 6: Evaluation of finding tags against the Chest ImaGenome silver dataset. We show image-level and region-level scores for different types of Chest ImaGenome findings. Note that we use different finding and region sets that are not perfectly mappable such that these results need be interpreted with caution. We still achieve a notable overlap, comparable to Chest ImaGenome's 's performance against expert annotations (Tabs. 2a and
Chest ImaGonnoe [MCC]
Classes
Anat. Find. Disease Tubes Device Miero
Ours (Image-level)
0.63
0.74
0.74
0.76
0.69
Ours (Region-level)
0.43
0.50
0.56
0.46
0.53
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Table 7: Evaluation of finding tags against the 13 CheXpert (CXP) classes from the MIMIC-CXR-IPP test set (Sec. 4.1. We show finding-level scores, macro-averages over subsets and the micro-average, with 95% confidence intervals (bootstrapping, 1000).
HTMIC-CX-JIPG..ao al.
2024n Test
[Precision]
[Recal]]
F1
|MCC]
Chest ImaG.
Ours
Chest ImaG.
Ours
Chest ImaG.
Ours
Chest ImaG.
Findings in CXP-S, CXP-7, and CXP-13
Atclectasis
0.82
0.78
0.99
0.99
0.9
0.88
0.84
0.81
(0.78: 0.87)
[0:13,083]
[050,
[0.98,
[0.87,0.93
[0.84.
(0.8.
[0.77.
Cardiomegaly
0.64
0.67
0.85
0.82
0.73
0.74
0.61
0.63
(0.58. 0:71
(061,0774
10:78
08.75,087]
10.67, 0.75]
0.78,00791
10.56,0.881
10.56, 0.691
Consolidation
0.83
0.77
0.87
0.93
0,85
0.84
0,83
0.83
[0.73,
[0.68, 0.36)
[0.79] 0.94
[0.85 0.99)
[0.78, 0.911
[0.78, 0.9)
10.75, 0.95
[0.76, 0.89)
Edema
0.94
0.9
0.8
0.8
0.86
0.85
0.83
0.8
10.900998
10,85,
10.73, 0.86)
10,74,0880
J0.82, 0.91
[0.8, 0.89)
(0.77,0871
[0.24, 0.85|
Pleural Elfusion
0.97
0.94
86,8, 0.93)
[0.62.09]
[0.66
[0.96,
[0.92, 0.96]
[0.89, 0.94]
[0.85,0.92]
[0.8, 0.89)
Findings in CXP-7 and CXP-13
Pueumonia
0.95
0.93
(0. (0.87,00 0.96
[0.84,0.44]
[0.80, 0.97
[0.91.0.8]
[0.80, 0.96]
(0 00.9,000 0.95.
10.87,0995
80.86 0.94]
Pucumothorax
0.79
0.84
0.89
0.8
0.84
0.79
00.61.0.89)
0.91)
(..5).0.55
(.976.098)
0.89]
(0.74,0.22
0.58)
[0.72. 0.91]
Findings in CXP-13
Enlarged Cardiom.
0.23
[0:4].08]
[0.29.0.51]
0.55]
(:.5.:445)
Lung Lesion
0.17
0.68
0.81
0.87
0.28
0.76
0,25
0,74
(0. 0.222
[0.56,
[0.09.
0:911
(0.76,
(0.5)
0.84
[o: 30.
[0.4 0.83)
Lang Opacity
0.62
0.28
0.83
1.0
0.71
0.43
0.61
0.2
00.56. 0.69)
[0.24,0.31]
(0.77.0.89)
[1.0,10]
[0.65.
0.76.
(0.30
0.49)
po. (6654.068)
[0.17. 0..17.
Pieural Other
0.54
0,87
0,92
0.67
0.45
0.67
0..61
00.19.0.22)
[.3]
[0.76,
[0.40.0.8]
0.58)
(0.51,0779)
(0.37.0.6)
Fracture
0.92
0.82
00.54.
00:47.0.73]
Loj
[0.68,
0.79]
[667.085]
[o
Support Devices
0.61
0.62
0,98
0.83
0.78
0.71
0.63
0,54
00.56. 0.66)
[0.56, 0.67)
[0.66 1.0]
0.88)
(0.71.
00.66.0.75)
[0.59, 0.68)
[0.48, 0.61]
Macro-averages
CheXpert-5 (CXP-5)
0.83
0.8
0.9
0.9
0.85
0.84
0.8
0,78
[0. 0.85)
[0:77,
0.92)
[0.88 0.92
[0.83. 0.87
[0.82. 0.86]
[0.77, 0.83]
[0 0.81)
CheXpert-7 (CXP-7)
0.83
0,81
0.89
0.91
0.86
0.85
0.81
0.8
0.86
[0.78. 0.84)
[0.87. 6.92)
[0.8 0.93
0.83
(1.83.0887)
(0.79,
(008 0.83)
CheXpert-13 (CXP-13)
0.69
0.67
0.85
0.85
0.74
0.72
0.69
0.66
(0, (0.66,00 0.713
0.75
[0.53,
[0.82 00.82.087)
12.72. 0.76]
(0.7.0744
[0.67.0.71]
(0 64 0.69)
Micro
0,68
0.63
0.89
0.88
0.77
0.73
0.71
0.67
[0.65,0.7
[0.61.0.55
[0.77 0.91
[0.85, 00.86.0.99
10.75, 0.79)
(0.72. (0.72.0.75)
(0.69.0.23]
[0.63. 0.68)
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Table 8: Evaluation of finding tags against the 13 CXP and 12 long-tail (LT) classes from the CXR-LT 2024 gold standard dataset (Sec. 4.1). We show finding- scores, macro-averages over different subsets and the micro-average, with 95% confidence intervals (bootstrapping, n 1000).
CXR-LT 2024 Holste et.
2025
Gold
[Precision|
[Recal]]
[F|]
|MCC|
Ours
Chest ImaG.
Ours
Chest ImaG.
Ours
Chest ImaG.
Owrs
Chest ImaG.
Fiudings CXP-S, CXP-7, CXP-13, and CXR-LT
Ateloctasis
0.55
0.56
0.82
0.99
0.66
0.48
0.59
[0.47. 0,62)
[0.69,061]
[0.75,0.8]
[0.77 1.0)
[0.59,0.71]
0,76.
[0.4.0.57]
(0.51,0.65)
Cardiomegaly
0.82
0.85
0.85
0.8
0.84
0,82
0.73
0.72
1076,
00.79,0911
1029, 0.11
(0.73, 0.86)
[0.79, 0.581
10.72, 0.871
[0.06, 0.791
10,60, 0.791
Consolidation
0,82
0.74
0.86
0.89
0.84
0.8
0.8
0,76
[0.73, 0,91
[0.63, 0.83)
[0.76, 0.93)
[0... 0.95
[0.77. 0.9)
[0.73, 0.87)
[0.72.0.87]
[0.67, 0.83)
Bddea
0.73
0.69
0.64
0.71
0.68
0.7
0.59
0.6
[0,63,0.33
0.791
[0.5500744]
[0.61. 0.81
[0.6. 0.766
10.62, 0.771
10.5.0.691
[0.5. 0.69)
Pleural Effiusion
0.82
0.78
0.93
0.97
0.76
[0.76, 0.87]
(0.75,0.44
0.970
[0.941 0..99
[0.83,0911
[0.83 0.91
0.83)
[0.7, 0.82)
Findings in CXP-7, CXP-13, and CXR-LT
Pneumenia
0.38
0.45
0.76
0.38
0.24
19.0.58)
10.07, 0.2170.313
10.25, 0,67)
[0.
(0.55,0.94]
0.58
[0 0.30.33
[0. 19,0.55
[0. 0:3044
Pncumechorax
0.85
0.8
0.85
0,96
0.85
0.87
0.83
0.86
[0.73,
[0.7.09]
(..22,
[0.39, 1.0)
(0.75.0.00
[0.8 0.94)
(0.72.0.91]
[0.78, 0.93)
Findings CXP-13 and CXR-LT
Enlarged Cardiom.
0,78
1.0
0.13
[0.57, 0.95
1.0)
[0.07.033]
33.
[0.1,
[0.13,0.35]
00.39
Lung Lesion
0.01
0.05
0.5
0.75
0.02
0.1
0.0
0.18
[0000.00]]
[00,0012]
[0.0 L.o]
[ax) Logg
(0.0) 006)
[0.0. 0.22)
[-0.09
[-0.02,
Lung Opacity
0.92
0.54
0,77
1.0
0.84
0.7
0.73
0.35
0.967
[0.49.0.59]
10.72, 0.84
[10.
0.58)
(0.66 74]
00.66.0.79]
[0.20, 0.4)
Pleural Other
0.11
0.23
0.19
1.0
0.14
0.38
0.45
[0.0, 0.24
(0.13.0.34]
0.43)
[10.110]
(0.0.0020)
[o 0.51)
[-0.65, 0-0.0.0.266)
0.26)
[0. 0.15)
Fracture
0.89
0.84
0.91
[0.72.004]
po.81 0.08
0.93)
9882.095)
[0:73.0.0) 0.9)
0.5)
Support Devices
0.92
0.93
0,93
0.83
0.92
0,88
0.83
0.75
[0.38, 0.95]
[0.9,0.97]
[0.89, 0..69.0.66]
[0.77, 0.88)
[0.9. 0.5)
[0.84, 0..11
(0.77.0.88]
[0.65, 0.82)
Findings in L'T-only, and CXR-LT
Calcification of Aorta
0.95
0.95
0.43
0.93
0.6
0.94
0.61
0.94
[0:33] [.0]
[0. 1.0]
[0.28 0.58]
[0.5. 1.0]
[0.45, 0.73
[0.80, 0:90]
[0.48. 0.73]
[0.87 0.99)
Emphysema
0.58
0.54
0.81
0.81
0,68
0.65
0.66
0.63
0.741
[0.38, 0.69)
(oust. 0.55
(0.63,
(0.63
(0.52, (8)
(0.4%.
(0.5.
(0.77
Fibrosis
0.27
0.0
0.52
0.0
0.36
0.0
0.33
0.0
0.15.00.43
0.00.000
00.31,0.74)
1..0
(0.21,0:52]
(0.0, 0.00
(0.17.0.5)
0.00
Hemia
1.0
0.86
0.9
0.9
0.95
0,88
0.95
0.87
1.0
10.73,
[0.73,
1.0)
[0.74, 0.97
10,5, 1o)
(..75
Infiltration
0.15
0.38
0.33
0.55
0.21
0.44
0.19
0.44
[003 0.3]
[o. 0.67
[0:08 0.7]
0.88
[0.05, 39]
[o 18, D.60]
[0.01, 0.39
10. 10.77,00 0.69)
Mass
0.54
0.28
0.78
0.89
0.64
0,42
0.63
0.46
[0.54 0.74]
[0. (0.17,0.4)
|0.56. 0.94]
[0.71. 1.0
[0.44, 0.78)
[0.28, [[56]
[0.45, (.745
10.S2 0.58)
Nodule
0.92
0.5
0.74
0.91
0.82
0.64
0.81
0.64
[0 1.g
[0.36, 0.63)
(0:57, 0.83)
[0.78. 1.0)
[0.68, 0.91
(0.51.0.75)
[0.68, 0991
10.S1
0.744
Pleural Thickening
0.78
0.33
1.0
1.0
0.88
0.5
0.88
0,54
[0.62
[0.22. 0.45)
1.00
[1.0 Loo
(0.77.096]
[0. 36, 0.62]
(0.78
0.64]
Preumomedinstiuum
0.94
0.88
0.84
0.84
0.89
0.86
0.88
0.85
[0:3, t.j
0.97
[07. (.966
[0.78, 066)
[0 0:944
(0.77.0955
0.93
Pneamoperitoneum
0,88
0.82
0,96
L.0
0.92
0.9
0.91
0.9
[0.72. 1.0j
[0.65, 0.96)
[0.85, 1.00
[1.0.
j0.81. 1.0j
(0.79.0.96]
[0.8.
[0.8. 0.98)
Subcur. Emphysema
0.97
0.0
0.8
0.0
0,88
0.87
0.0
[0.5 1.0j
(0.0.000
[0.68, 0.93)
[.00
[0. (0.79.095)
(o.0, 0.00
[o. (0.7)
(.0)
Tortuons Aorta
0.83
0.79
0.88
0.94
0.85
0.84
0.85
69, 0.95]
[0.64,
[0.75, 0.97]
[0.54] 1.0g
(0.75.0933)
10.75, 0:94]
(0.73.0.93]
0.93)
Macro-averages
CheXpert-5 (CXP.5)
0.75
0.72
0.82
0.87
[0.7]
[0.69, 0.76]
0.55]
[0.54, 0.9)
[0.75.08]
[o
[0.64,
0.72)
CheXpert-7 (CXP-7)
0.71
0.65
0,77
0.87
0.73
0,71
0.68
0.65
|0,0,
[0.02.
0.681
(0.3.
1033.
(.777
10.09,
(0.61,
[0.01. 0.68)
CheXpert- 13 (CXP-13)
0.66
0.63
0.68
0.81
0.663
0.62
0.57
0.56
[0.61. (.699]
[0.61. [0.61.065]
(ou61.
(0.75 0.85)
(0.6.0665
[0.6 0.64]
[0.54,
[0.54 0.59)
LT-only
0.73
0.53
0.75
0.73
0.72
0.59
0.71
0.59
[0.60 0.77]
0.57)
(.57
[0 60.000 0.77
10. 0.75
[o 0.62]
[0.67.074
[0.55, 0.63)
CXR-LT
0.58
0.71
0.77
[0.67, 0.72]
[0.56
0.75]
[0.74, 0.8)
[0.65, 0.69)
10.58 0.62]
(0666.
10.55, 0.6]
Micro
0.69
0.62
0.76
0.64
|0.67,
0.64)
0.821
(..71.074)
10.68, 0.72]
0.69
0.66)
22

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Published as a conference paper at ICLR 2026
C.3 SCENE GRAPH EVALUATION: FINDING BOXES
Table 9: Evaluation of finding bounding boxes against 6 finding classes from MS-CXR (see Sec. 4.1 We show finding-level scores, macro-averages over different subsets and the micro-average. with 95% confidence intervals (bootstrapping, 1000). We excluded 2 of the 8 finding classes, because there are no samples that have positive annotations from MS-CXR, Chest ImaGenome and our dataset.
MS-CXR Boccking et al. 2024
Iooe 30)
[ot @ 301
Ours
Chest ImaG.
Ours
Chest ImaG.
Ours
Chest TmaG.
Atelectasis
0.5
(0.12,0.42]
1002,
(0.34,
10.05, 0.25
1071, (071.093)
(..74.0944)
Candiomegaly
0.96
0.97
1.0
1.0
0,96
0.99
[0.93.058]
[095, 0.99
[10.10]]
[1.0.10
[0.3 0.98]
[0.98 1.0)
Consolidation
0.31
0.2
0.41
0.24
0.91
0.98
[0. 00.19.045]
[..10.331]
[0.20.0.4]
[0. (2.02.335
[0.81.
[0.93. 1.0]
Eidena
0.52
0.52
0.52
0.52
1.0
1.0
[0.32,
[0.32,0.71]
0.11
(0.32,0.71
[1..110]
(1.0.10)
Pneumonin
0.28
0.58
0.34
[0.41.0.57]
[0.31. 0.35)
[0.5,0066)
0.97]
[1.0, (]]]
Pncumothorax
0.14
0.15
0.14
0.15
0,96
0,98
[0:1,0.88
(0.1,0.19)
[o.11,02]
[a98, 0.98)
[0396 1.0]
Macro
0.45
0.37
0.53
0.4
0.93
0.97
[0.4.031
[0.38
(0.88.0.88)
|0.36, 0.44
10s, 0.95)
[0.95,
Micro
0.51
0.45
0,56
0.48
0.94
0.98
[0. 0.54)
[0-42, 0.49]
[0.52. 0.6)
[0:45, 0.52]
(0.92 0.96]
[0.97.0.99]
Table 10: Evaluation of finding bounding boxes against 18 finding classes from REFLACX (see Sec. H.I) We show finding-leved scores, macro-averages over different subsets and the micro-average, with 95% confidence intervals (bootstrapping, n. 1000). Note that we excluded 11 of the 29 finding classes, because there are no samples that have positive annotations from REFLACX, Chest ImaGenome and our dataset.
REFLACX
Bigolin Lunfredi al.
2021 all phases
[loU @ 30]
[loP@30]
[loT@30 30]
Ours
Chest ImaG.
Ours
Chest ImaG.
Ours
Chest ImaG.
Absormal mediastinal contour
0.08
0.25
0.08
0.25
1.0
1.0
0,31]
[0.0,0.57]
10.0, 0.311
[0.0 0:57)
[1.0.
[L.0 1.0]
Acute fracture
0.0
0.0
0.0)
[0.00
[0000
[1.0.
Atelectasis
0,29
0.15
0.47
0.2
0.76
0,93
[0.26,
(0.12, 0.17)
[0:44, 0.531
[0.17.0.2) 0.23)
(0.75,0778)
10.91.
Consolidation
0.39
0.27
0.51
0.34
0.8
0.95
[0:33, 0.45]
(0.22, 0.32)
0.57)
[0.2% 0.4)
10.74, 0.851
10.92 (.57)
Emphysema
1.0
1.0
1.0
1.0
1.0
1.0
[1.0 1.0
[1.00
1.00
[1.0,
[1.0 1.0)
Enlarged cardiac silhouette
0.96
0.96
10.4,
10.55,
0.971
10.99,
1.0)
10.95,
10.9,
Enlarged hilum
0.5
0.5
0.5
0.8
10.0.
[00) 1.0)
10.0 1.0
[0.00
10.0, 1.0)
Fracture
0.0
0.0
0.0
0.0
0.0
0.0
10.0, 0.00
10.0 0.00
10.0. 0.00
0.00
10.01 0.00
J0.0, 0:01
Groundglass opacity
0,28
0.31
0.48
0.38
0.77
0.96
[0.22 0.34]
0.25,0038
10.4 0.54)
10.32 0:45)
(0.71,
10.93, 01.9)
Hiatal hemia
0.4
0.4
0.94
10.0, 0.43]
(0.17.
(..06.
10.13, 0.67)
10.18, 2.01
11.0,
Hlghh lung volume / emphysema
0.48
0.58
0.58
0.58
0.9
1.0
[0.25, 0.7]
[0.35, 0.79)
[0.35, 9.79)
10.75. 1.0)
[L.. 1:0]
Interstitial lung disense
0.5
0.8
0.8
0.8
0.8
1.0
10.0. 1.0)
[00) 1.0
10.0. 1.0
(a0. 1.0)
10.12. 1.0
11.0, 1:0]
Lung nodule
0.18
0.09
0.21
0.09
0.89
0.91
[0.0) 0,31]
[0,02, 0.2)
1..5.
[0x0) 0.2)
(0.17,097)
10., 0.95)
Pleura abnormality
0.16
0.2
0,17
10.13. 0.191
(0.11.0117)
00.17.0.233
10.14, 0.2)
(0.88. 0.93)
[05s, 0,344
Pleural elfusion
0.42
0.37
0.53
0,53
1.0
0.95
10.2 0.65]
0.61
0:75)
(0.31.
[1.0. 1:0
10882 1.0]
Pleural thickening
0.0
0.0
0.0
0.0
1.0
1.0
[0.0, 0.0)
[0.) (0.0000]
10.0, 0.0
(00 0.00
[1.0. 11.0.10)
[1.0, 1.0)
Preurnothorux
0.04
0.13
0.04
0.13
0.9
0.96
10.01, 0.08]
(0,07, (0.07.
0211
10.01.
0.00)
10.07
0.211
10.84, 0.60)
10.92, 0159)
Pulmonary edema
0.51
0.58
0.55
0.58
[0.45,
[0.55, 0.63)
[0.5. 0.6)
[0.53, 0.63)
10.93, 0.97
Macro
0.34
0.37
0.41
0.41
0.84
0.87
00.27,0.47 0.42]
[0.5.0.45]
10.34, 0.49
(0.33,0.40
00.78.091]
10.8 0.95]
Micro
0.45
0.42
0.54
0.46
0.87
0,95
[0.44. 0,47]
(0.4.043)
[0.53, 0.56)
[0.44. 0.47)
[0.86, 0.88)
10.91, 0.96]
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D DATASET STRUCTURE
D.1 SCENE GRAPH STRUCTURE
Region
--O.n-
ReportGraph
0..1
Indication
region_region region
relation_ lype
O..n
0..n
top_level
distance
O.n
located at
Observation
obs. relation
0.n
child_type
o..n
Sentence
Figure 13: Scene graph structure overview.
Sentence Nodes
Sentence nodes are directly associated with raw sentences in the report, i.e. there exactly one sentence node per identified report sentence. They contain the following attributes:
sent _ Identifier, unique per study.
Example: S01.
section: Name of the section that the sentence belongs to, specified in the report. If the sentence not part of a section, FTNALLBRPORT.NNOSCCTTT or are used.
Examples: WINDINGS, IMPRESSION, REASON_FOR_EXA
The identified type of section used for classifying the type of content of the sentence. IGNORE is used for irrelevant sections.
section.type:
Examples: FINDINGS, IMPRESSION, INDICATION.
sentence: The sentence written in the report.
Observation Nodes Observation nodes are created for each individually described aspect (i.e. observation) in the report's FINDINGS IMPRESSTON section. Hereby, single sentence may be related to several observation nodes and single observation may be derived from several sentences (if they describe related aspects). Observation nodes are structured hierarchically, i.c. they may have other observation nodes as parents. An observation node contains the following attributes:
obs iid Identifier, unique per study.
Example: 001.
For child nodes this also contains the parent id, e.g. 001. 02.
cermmarr-seetteeeee Textual description of the observation, directly derived from the associated report sentences. In some cases, this may be an exact copy of the report sentences but it may also paraphrase parts of it.
name: Abbreviated version of the memumarr-senence...
leved Hierarchy level, 0 top-level, larger numbers deeper hierarchy levels.
child type: Type of parent-child relation.
Possible options:
regional dist i nat ion, re llated region, associated.uv a th, device recommendate ion, compa son_on
regions: List of associated regions, each paired with an optional list of distance annota- tions.
Example:
[ ("heart", ["1 cm above"] [
non resolved regions: Similar to zegions but with regions that could not be semantically mapped reference definitions.
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laterality: Laterality of the region.
Possible options: left, right, likely bilateral, bilateraa, unknown.
default default.regions: List of regions that have been added because they are defaults for the identified findings (ibssennities))
obs.entities: List (directly) associated findings.
Example: ["pleural effusion").
obs entities parents: List of findings that are considered parents of findings in obs.entities:
non resolved obs entities: Similar to obs_ ibs_entities but with findings that could not be semantically mapped to reference definitions.
obs cbs.caeeeooiss List associated finding super-categories.
Example:
obs ubs.suubcategoriis: List of associated finding sub-categories. Example: ["LUNG_FIELD"
probability: Likelihood of the observation being positive. Short term, derived from what is mentioned the report.
certainty: How certain is the observation. Derived from probability.
Possible options: certain, likely, uncertain, comparison.only;
recommendation.
positiveness: Whether the observation is positive negative. Derived from probability.
Possible options: pos, neg, comparison.onlyy recommendation.
modi fiers: Modifiers of the finding. Dictionary with keys for each type of modifier and lists individual modifier values.
Possible modifier type: severity, texture, spread, temporal.
Example: ("severity": ["mild"], "spread": ["focal"]]:
change.sene sentence: Optional textual description of any changes the prior study of the same patient, if was mentioned in the report.
changes: List of change types mentioned in the change.sentence. Example: worsening.
from report: Whether this observation was explicitly mentioned in the report (true) or automatically added (false) sse).
obs quality: Extraction quality of the observation, consisting of several individual aspects. See Tab.
localization: Bounding boxes for this observation, for each associated image. Dictio- nary with keys equaling image ids (each study may correspond to several images). Values contain:
image.id id
bboxes: List of bounding boxes in the (x1, y1, 22, 12) format original image-pixel coordinates.
reeeeeeneiiorrffeerenneeeeee ids: List of region names from which the bounding boxes are derived.
missing localization! List of associated region names for which no localiza- tion is available for this image.
fallback: Whether this localization a fallback, i.e. the original region local- ization was not available but more coarse localization was used instead.
locallizationn iut.iiioonuuuty.:........... Quality of the localization. See Tab. [1
Region Nodes Region nodes are created for each anatomical structure mentioned any observation and for key regions. They contain the following attributes:
region: Name the region and unique identifier within each study. Example: left lung.
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laterality: Laterality of the region.
Possible options: left, right, bilateral, unknown (i.e. not clearly definable).
localization: Bounding boxes for this region, for each associated image. Same format for observation nodes.
region localization quality: Quality of the localization attribute. See Tab.
Indication Node Each study contains an optional indication node with information extracted from the INDICATION section. If present, it contains the following attributes:
indication.sumaary Summary of the indication, directly derived from the INDICATION section of report, but typically paraphrased.
patient.img :nfo: Any information about the patient, if mentioned in the INDICATION section. A subset of the content in Indicat paationssumma on_summary.
indication Indication for the study, if mentioned in the INDICATION section. A subset of content indi cat consummarr.
evaluation: Any required evaluation of the patient (i.e. what should be evaluated with this study), if mentioned in the INDICATION section. A subset of the content in indi cation summary.
associaatd ids: List of sent_ids from sentence nodes that are related to the indication.
associateddobb.icc List obs_ids from observation nodes that are related to the indication.
answer for indication: A single observation node containing the answer to the question (implicitly) asked by the provided indication. This is special observation node with obs_id OIND, Its textual description directly derived from the FINDINGS and IMPRESSION sections but conditioned on the INDICATION section.
Root Node and Relations Each study contains a single root node called the ReportGraph. contains general metadata about the study and its scene graphs:
patient id: Unique patient ID,, the subject. id from MIMIC-XXR.
study_id: Unique study ID, from MIMIC-CX, Each patient may have several studies.
study quality: The overall extraction quality of the scene graph for this study, consist- ing of several individual aspects. See Tab. [T
study img localization quality: Dictionary of localization qualities for each image with keys corresponding to image IDs. See Tab.
Additionally, it connected all other nodes and links to the top-level (root) observations. Thus, contains the following:
sentences: List of all sentence nodes.
observations: Dictionary of all observation nodes, indexed by their obs_id.
top level obs ids: List of all top-level (root) observation node IDs, i.c. their obs ids.
regions: Dictionary all region nodes, indexed by their region on attribute.
indication: The indication node, exists.
Nodes also be connected by the following relations:
located_at setated_at_relaaeee at.relations (observations)) ionittigiiootte))) Specifies where observation is located with the following additional attributes:
distances:
List of distance annotations, e.g. ["3em above"].
- where specified: How this relation was derived.
Possible options: di rect, bi lateral, sub region.
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obs relaaiiis
Specifies parent-child relation between two observations, with the following additional attribute:
cyie:.type: Type of parent-child relation.
Possible options:
relatedrrgginn, associated with, device part, recommendaiion comparison only.
degionall.iistinntiin
wes.sent_reeatioon (observation++sentenne)) Specifies from which sen- tences observation was derived.
regiion.region..eaationn (region+rreioon) Specifies relation between two regions with the following additional attribute:
relation.type. Type of relation.
Possible options: sub- wub.rregion vegion, bilateral (the bilateral version of region), left (the left version region), right (the right version region).
D.2 QUESTION-ANSWER STRUCTURE
Each question-answer pair consists of a free-text question (attribute question), an answer consisting of structured answer parts (attribute answers). Additionally, it contains the following metadata:
QA-Pair
question id: Identifier, unique within the associated study.
question.tye The QA-template used generate this QA-pair.
The strategy used generate QA-pair. See Secs. 3.2 and E.2.2
variables: Key-value pairs of variables (and their values) used during generation, e.g. to fill the template. See Sec. E.2.1
obs_ids: List of obs_idss idss of observation nodes (in the scene graph) from which the
answer is derived.
contains rwwpest.errt.tooee answers: Whether any of the answer parts was derived from the report, i.e. from observation nodes.
contains wewpainttteelate..mmmmmm anwwers:: Whether any of the answer parts was generated based on template.
extraction ierraction.uualit... The overall extraction quality of the associated observations in the scene graph, consisting of several individual aspects. See Tab, [
quality:: Quality of the localizations per image. See
Tab. [I]
question.qualityy
The overall question-answer text quality, consisting of several
individual aspects. See Tab. I2
rating: The overall rating of the QA-pair. See Sec. D.3
Answers are structured hicrarchically, consisting a list of answer parts (attribute answers) and sub-answers (children) of these answers, where there be several hierarchy levels. The hierarchy levels are derived from the parent-child structure of associated observation nodes (based on obs abs_relane www.eellliins.... Sec. D.I Additionally, there are different types of answer parts:
main answer: Required to answer the question. There is always at least one main- main-answer per question.
details: Providing additional details for the main answer, which are however not manda- tory to answer the question.
related informationn Not directly answering the question, but may be related and provides context.
Each individual answer part contains the following attributes:
answer id: Identifier, unique within each study. Contains the questi on i.d.
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text: The answer text. Either generated from template or based sunmary.sentence in the observation node (Sec. D.I.
answer.leveli Hierarchy level, 0 for top-level answer part, larger numbers for deeper hierarchy levels (sub-answers).
answer type: Type of answer part.
Possible options: ma main.answerr ansser, details i s,
name tag: Abbreviated version of the text. Either generated from template or based on name in the observation node (Sec. D.I..
laterality: Laterality of the region. See observation node (Sec. D.I
Possible options: left, right, likely bilateral, bilatera, unknown.
regions: List of associated regions. See observation node (Sec. D.1 Distances are not provided here.
Example: ["heart"]
obs entities: List of (directly) associated findings. See observation node (Sec. D.I Example: ["pleural effusion").
List of findings that are considered parents of findings in obs_entities, See observation node (Sec. D.I..
obs antttiies.aaeeeeeee parents:
obs categories: List of associated finding super-categories. See observation node (Sec. (Sec.D.I) [.I].
Example: ["ANATOMICALLPINDINC""
obs subcateggories: List of associated finding sub-categories. See observation node (Sec. [.I]
Example: ["LUNG FIELD").
certainty: How certain is the observation. See observation node (Sec. D.I.
Possible options: certain, likely, uncertain, comparison_ont
recomeendation.
positiveness: Whether the observation is positive or negative. See observation node SSec.D.I).
Possible options: pos, neg, comparison only, recommendation.
modifiers: Modifiers of the finding. List of pairs of modifier type and value. See observation node (Sec. D.I.
Possible modifier type: severity, texture, spread, temporal.
Example: ("severity", "mild"), ("spread", "focal")
localization Bounding boxes for this answer part, for cach associated image. Dic- tionary with keys equaling image ids (each study may correspond several images). See observation node (Sec. D.I.
sub answers: List of child answers (deeper in the hierarchy). Each sub-answer is another answer-part with all attributes and potentially further sub-answers.
from report: Whether this answer part is derived from the report, i.e. an observation node (true), or from a template (false).
inttractionnquualitte. The overall extraction quality of the associated observations in the scene graph, consisting of several individual aspects. See Tab, []
answer.qualle quserr.qualit:: The overall answer text quality, consisting of several individual as- pects. See Tab. 12
D.3 QUALITY
Ratings
We distinguish between the following overall ratings for each QA-pair:
A++: Perfect and complete content; all information in the answer is explicitly mentioned in the report.
A+: Perfect and mostly complete content; all information in the answer is explicitly men- tioned in the report, but some minor details may be missing irrelevant.
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A: Very good content with minor issues not affecting the overall quality; some tags or boxes may be inferred or minor issues (e.g. grammatical) may be present in the text.
B: Good content; factually correct answers, which may however not fully complete or slightly unclear.
C: Poor content; answers may be misleading contain completely unclear information.
D: Incorrect content; answers may be contradicting the report and usable.
not rated: Quality could not be assessed, e.g. due invalid L.LM-rater outputs.
These ratings are derived based individual aspects that will be described in the following paragraphs. Possible quality levels for each aspects and the resulting rating are presented Tabs. []] and 12 The final rating is computed as the minimum (worst) rating all individual aspects.
Scene Graph Extraction Quality For each scene graph, we provide quality rating based on how well it could constructed//xttracedd. Tab. shows the considered aspects with their potential quality levels and resulting ratings.
Table 11: Quality levels for the 6 scene graph quality aspects, with their resulting ratings.
Quality level
Resulting rating
How well are region tags identified? (attribute regioas)
No RESIONS
B
Region extraction
DEPAULT DEPAUITTREGIONSS TREGGONNS_NNT
CONTATINS WwW.-RRROOOODDGGIOOOO
RECTONS
CONTAING WWE.H/NOOO.RRRR
A
RESOLV WWW.ROOEERGNSNOONOOOOOOO
A++
How well are finding tags identifled? (attribute obs ent intities)
Finding extraitiio
NO ENTITIES
CONTAINS RESOLVED.ENNITEEE
RESOTTED ENTTTTRS ONLY
A++
How well are textual descriptions extracted? (attributes surma commmncyy.neettmeee enceneennnnn and name)
Description extraction
CHANGE_IN_GENEE ERL.GENEENCE.OR.R
B
UNDERSCORES SENTENCE
NO.ISSUES
How well are mentions of change extracted? (attributes change sereeseentl www.eeeeercc Lecce and change)
BHLANGE_SENE2A NELLLEEEEEEE RENOVEDD
B
Chunge extraction
UNDP RSCORES_ TM_CHANCF_SRINTRR
CONTAING WwW.:IOO..O.RREELEEDDD CHNANGE
A
1SSUES
A++
Have there been any issues in the extraction and scene graph construction pipeline?
DISCARDEC
D
NON.INTERPRETAALL
Extraction
lssues
MOSTIY_TNTERRPE WWW.TRRRREEAATRRR
B
IGNORABLE
2
A
PIXABLS
3
A+
NO.ISSUES
A++
How could observatioos/regions localized? (attribute locelization:)
NO.LOCALI ZATION
B
Localization
PALLBBAC..LCCALTAATT
B
TNCOMPTLETE TACAI.TSATTON
2
A
DOOX_LOCALT2ATIO
A++
BSUX_AND JMAK LOCAL 1EATLO
A++
Finding extraction entity extraction, description extraction sentence/name quality.
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QA Text Quality For each QA-pair, provide quality rating for its text, i.. the question text and the textual descriptions in its answer parts. Tab. 12 shows the considered aspects with their potential quality levels and resulting ratings.
Table 12: Quality levels for the 5 QA-pair text quality aspects, with their resulting ratings.
Quality
Resulting rating
Does the answer factually align with the original report?
(rated per answer part, given the question and the report)
NON-ALIGNED WA..GNED.COOTRADOOOO .CONTRADICCING
Excallmeux
NON.ALIGNBO INN.IIEERADE
ALIGNED.GENER SA..TAAA..TTTEET
A
ALIGNED.NEGATIVO WEW.TTVVENOT.MENTTOOO
A+
ALIGNED INFERABLE
A++
ALIGNED_MES IONED
A++
Is the relevant for the given question?
(rated per part. given the question but independent of the report)
IRRELEVANT.CRFF
-2
A
Relevance
REDUNDANE_INPO
A
RELATED INFO
A+ (A++ for related informet ion answer)
RELEVANT MAAIVVAAN.NANNANNWWEE..
A++ (A for selated.informet lon answer)
Does the answer cover all aspects in the report that relevant to the question?
(rated for the full answer, given the question and the report)
THOOMPIRTT..CC STT._RST..AAATNN
Completaness
FULLY WWLLY.COMPLLA LETE
A++
the generated question clear and grammatically correct?
question, given nothing else)
UNANSWERABLE
Question clurity
UNRELATED. WWW.TTTT.COTTTTECO
UNCLEAR-QUESTION
B
GRAAMATTCAL_ERRRN
A
A
OPT-NAL
A++
Is the clear and grammatically correct?
(rated per answer-part, given nothing clsc)
NUT JNDT
-2
Answer clarity
SINCTFFARANNSER
GRAMMATICAL
UNUSUAL. WWNSSUAL.GNNENCC... SEN.ENCE.STRRCCOOOOO
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E DATASET CONSTRUCTION DETAILS
E.1 SCENE GRAPH CONSTRUCTION
E.1.1 REGION LOCALIZATION
We use the CXAS Seibold et al. 2022 2023) model to predict segmentation masks of 158 anatomical structures the 377,110 CXRs from
(Johnson et al. 2024a 2019b Goldberger 2000). Additionally, we use the bounding boxes provided by the Chest ImaGenome (Wu ct al.
2021a bt Goldberger et al. 2000) dataset, which are provided for 29 anatomical structures in most frontal images of MIMIC-CXI The masks predicted by CXAS are post-processed with morphological operations filter outlier pixels.
al.
We specify 257 localized regions in our reference definitions. For each of these regions, we define how the bounding boxes are derived. We consider the following options:
CXAS masks: Some regions are directly associated with one of the 158 anatomical struc- tures for which the CXAS model predicts segmentation masks. In these cases, we compute the bounding box around the predicted segmentation mask.
Chest ImaGeneme boxes: Some regions are directly associated with one of the 29 anatom- ical structures for which Chest ImaGienome provides bounding boxes. In such cases, we use these provided bounding boxes if no CXAS masks are associated.
Bilateral regions: Some regions refer to a pair of bilateral regions (c.g. lungs refers to left lung and right lung). In these cases, we simply use the two bounding boxes of the left and right versions, but do not fuse them.
Parent regions: For some regions we do not have exact correspondences to available masks or boxes but have available sub-regions, In these cases, we compute the super bounding box, a single box, around all specified child regions.
Fusions: some rare cases, we combine multiple individual masks or bounding boxes. We compute intersections unions of boxes masks, before inferring the final bounding box.
After computing all regions, we filter out regions with a too small bounding box area. For images where a specific region is not available, we try to use alternative regions as fallbacks instead, e.g. using more coarse parent regions an alternative. Note that this is often the case for lateral images as there no Chest ImaGenome boxes are available.
1.1.2 INFORMATION EXTRACTION
Extracting the Sentences First, we extract individual sentences from the reports, detect their sections (e.g. FINDINGS, IMPRESSION, INDICATION, discard sentences without relevant information, and merge sentences containing similar information (e.g. findings are described in both the FINDINGS and IMPRESSION section). Therefore, each full report is passed in a single step to the LLM, which predicts the individually separated sentences as well as their sections and related sentences. We use the prompt shown in Listing [ (with few-shot examples similar to Listing 22 and apply it to the full radiology report. After parsing the LLM outputs, we apply the Stanza (Qi al. 2020) tokenizer each identified sentence and try further split it. The LLM also identified potentially related sentences. We use this information to identify sentence clusters containing related information. Such sentence clusters are the basis for the next step, i.c. observation extraction. We successfully extracted sentence from 227 626 studies (reports) while having parse errors for 209 studies.
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Listing 1: LLM prompt used for sentence extraction.
Extract all sentences from the given textual report.
You will be given (free-text) medical radiology report describing one chest X-rays of single patient.
# Rules:
Split the report into sentences and extract all sentences in the report
rewrite sentences!
For each sentences, identify the its section name (written in the 4 report).
If sentence is not part of section but is part of the "FINAL REPORT", SFINAAL_REPORTYLUE then "FINAL' REPORTE NOVORRTTTOOLLL SEV_SENCTON"". If sentence is part a section but the sentence is before the "FINAL REPORT", use the section name
For cach sentence, classify the centent written therein into one of the following types: [EXAM] TECHNIQUE INDICATION, FINDINGS, IGNORE] This is typically inferred from the section name but may also be influenced by the content the sentence. Some example sections names for each type are given
IMPRESSION, PRE\_FINALI_
below:
EXAM TECHNIQUE: EXAMINATION, EXAM, TECHNIQUE
INDICATION: INDICATION, INDICATIONS, HISTORY, CLINICAL HISTORY, 4 CLINICAL, REASON, REASON FOR EXAM
FINDINGS: FINDING, FINDINGS
IMPRESSION: IMPRESSION, IMPRESSIONS, RECOMMENDATION
PRE\_FINALY, REPORT: WET READ, WET NET'_READ> VERSIONI_ #1, PRE FINAL
REPORT SECTION
IGNORE: COMPARISON, COMPARISONS, REFERENCE EXAM, NOTIFICATION
Split the report into individual sentences and report each sentence in
+ its line, removing any newlines present in the sentence.
For enumerations: each point is considered an independent sentence!
+ Remove the numbering.
Specify sentence IDs of similar, previous sentences that each sentence could be merged with. A sentence should be merged with all previous sentences that either describe the
aspect or that refer each other (e.g. if sentence provides further details to previous one). A bullet point may also be associated with sentence, even if the other sentence has a different bullet number none all.
Follow the examples given below!
Examples:
<EWSHOT>>
Input Report (extract data from this report):
START OF REPORT
REPORT>
AND OF REPORT
Hints:
Infer the output format from the examples!
Do not add any explanations text BEFORE AFTER the extracted
sentences, i.e. start with the first sentence!
Proceed:
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Listing 2: Few-shot example for sentence extraction.
E++amelee: Report*** START OF REPORT
FINAL REPORT
PORTABLE CHEST OF LLL
COMPARISON: LLL radiograph.
FINDINGS: pleural effusion or pneumothorax. END OF REPORT
* ExEExpllee 5: Output
[SO1] FINALA REPORT NOV SECTION (EXAM) [EXAMLTECCNIIUUE merge with []:
PORTABLE CHEST LLL
[S02] COMPARISON (IGNORE)
merge with []: LLL_ radiograph.
[S03] FINDINGS (F INDINGS) merge with []: No pleural effusion or
pncumo hhrax.
Extracting the Observations this step, we consider each sentence cluster (as identified during sentence extraction), in the FINDINGS and IMPRESSION sections. A sentence cluster contains one or more sentences that describe related aspects and may stretch over one both of these sections. extract mentioned observations using the prompt shown in
From each of these clusters, Listing [ with few-shot examples similar to Listing 4 We apply this prompt to each sentence cluster individually and extract zero, one, multiple observations each. The output is provided in the json-format and follows a similar structure as the final observation node, but we optimized it to be easy fill by the LLM. The LLM is allowed freely assign values each of the json-fields. For name and summary sentencee we prompt the model to stay close to the original sentence, but it must remove any mentions of change and only keep the part relevant the individual observation (if several observations are mentioned in one sentence). We successfully extracted observations from 227 266 studies (reports) while having parse errors for 360 studies.
Listing 3: LLM prompt used for observation extraction.
Extract structured information from the given textual report. You will be given sentences from (free-text) medical radiology report describing
chest X-rays of single patient.
Guidelines:
<GUIDE>
# Rules:
Follow the examples given below!
Examples:
<EWSHOT>>
Hints:
Check for any "change" modifiers (see guidelines). If there is "change" modifier. rewrite the Soummryy__eeteence such that it describes only what is visible in the current image, without any mentions of change or comparisons! Describe the change in the
Do this for all top-level AND child
observations.
Make sure to include all children of observations, even if they repeat information from the parent!
Proceed with the Input Sentence: Sentence (s) <SENT> Output JSON-List:
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Listing 4: Fhw-shot example for observation extraction.
Sentence (s) Left more than right basilar atelectasis. Output JSON-List:
"ame": "bibasilar atelectasis", "entity": "atelectasis", "probability": "positive", "change"; null, "Bibasi lar atelectasis.", "change =_entence": null, "regions"; ("bibasilar"), "children":
"child' "child'Ltype": "regionall, "regional\_disttnntti _istincciion,, "name": "left 4 basilar atelectasis", "entity": "atelectasis", "probability": "positive", "change": null, "Left more than right basilar atelectasis.",
"change\_senten null, "regions": ["left basilar"]
Extracting the Indication Next, extract information about the INDICATION section and detect which FINDINGS or IMPRESSION sentences may provide information related to the indication. Therefore, the extracted INDICATION sentences and list of all FINDINGS and IMPRESSION sentences are passed to the LLM using the prompt shown in Listing 5 with few-shot examples similar 6 The LLM predicts a json-structure containing several in the indication, an answer for indicat's i on derived from the FINDINGS and IMPRESSION section, well as relevant sentence IDs. We successfully extracted indictions from 227 596 studies (reports) while having parse errors for 30 studies.
fields for summaries of aspects
Listing 5: LLM prompt used for indication extraction.
Extract structured information from the given (free-text) medical report. You will be given the indical cation sentence from a report and additionally 4 the sentences from the findings section.
# Rules:
Extract summarize the given indication information. Use only the 4 provided indication sentence.
Additionally, identify the finding sentences associated with the indication, 1.c. the sentence that answer the quesiton of the indication er are highly relevant to it. Based on these finding sentences, provide an answer to the question asked the 4 indication.
Follow the examples given below!
Examples:
<FEWSHOT>
# Hints:
For each attribute, write full sentences instead single terms + bullet points.
In the Lonaweri_for__i _oor___idiiaaiinn,, describe in YOUR OWN WORDS how the
+ question asked in the evaluation can be answered based on the findings. Only include the key information.
Use the JSON structure from the examples!
Proceed with the Input:
Innut::**
INDICA INDICATION <IND>
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FINDINGS:
<FIND>
*+Output A
Listing 6: Fho- example for indication extraction.
++Inuu:***
INDICATION:
LL with new onset ascites eval infection
FINDINGS:
[S01] There is no focal consolication, pleural effusion pneumothorax.
[S02] No acute cardiopulmonary process.
*uttpu *
"patient \_info"; "female",
"indication: "New onset ascites.",
"evaluation" "Evaluate for infection.",
"indication', irseiiin'ssummrry" "Female with new onset ascites; should be 4 evaluated for infection.",
[*S02"],
answer ionnee__toonnnniittiitinn "Evaluation for infection negative: There is no acute cardiopulmonary process."
E.1.3 BUILDING SCENE GRAPHS
Entity Mapping We apply semantic entity mapping to modifiers (used to fill the at- tributes probability, certainty, positiveness, and modifiers), regions (attribute regions) finding entities (attribute obs entities), and changes (attribute changes).
For each these we consider the associated tags extracted by the LLM during observation extraction and encode them into text embeddings using the BioLORD (Remy et al. 2024) model. We also encode all potential tags and their synonyms, defined for each type of tag in our reference definitions. Then compute the cosine similarities of each tag with all reference tags of the same type. We pick the reference tag with the highest cosine similarity but threshold it at 0.5. no reference tag was identified with cosine similarity 0.5, then mark the tag non-resolved. For finding entities, follow a slightly more complicated matching approach. Instead of only considering the finding entity tags extracted by the LLM,, we also consider pairs of these entities and extracted region tags well as the extracted summary sentences and names for matching. We then try to match each of those with the reference finding tags and pick the ones with the highest cosine similarities.
The matched reference finding tags are stored in the obs .en iie attribute (non-resolved ones are kept in mon_reso wed__bss_ede obs_entii ies), matched reference regions stored in the regi ons attribute, where we also store the distance as identified by the LLM (non-resolved regions are kept in non_resoll www.resolvedreerr Led_regions)) The matched changes are store in the changes attribute (non- resolved changes are discarded). For all modifiers, we use the modifier type defined for the matched reference tag. We matched all modifiers against all types modifiers, which means that the modifier type identified by the LLM can be overwritten during matching. Finally, we extract the probability from the modifiers (this a special modifier type), store in the probabill iitty attribute and infer the certainty and pos i viveness attributes from it (using the reference definitions). The remaining modifiers are stored in the modifiers attribute (non- inon-matched ones are discarded).
We additionally try to identify the laterality of the observations. Here, do not semantic entity mapping but rely on keywords instead. We consider the raw finding entities, regions, as well as the summary sentences, and search for any laterality related terms. From this we infer the laterality and store it into the laterality attribute.
mentions such as left, right, bilateral, and
Reference Data and Standardization Using the reference definitions, all obs entities parents, obs categories, obs subcategories, and default regions from the matched obs entities.
infer
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Next, we inspect the summary- -eeaarree-eeeeeeeeeee and name attributes (extracted by the LLM) for under- scores or mentions of changes. We track such issues (which are used for quality assessment) but do not upply any cleanup. Similarly, check the change sentence for underscores and assert that contains mentions of changes.
We further inspect the structure of observations and their children. If an observation mentions multiple different findings and has one child for each of these findings, then we lift these children to the top-level and discard the parent. Similarly, we merge multiple duplicate observations into one.
Finally, we try to resolve missing regions or improve their precision. If no regions could be extracted, rely the faulutt couuttrree reggt.rrriiie ons derived from the obs_entities iintttiiss instead, but consider the identified laterality. We also check whether these default.region extracted ones. Then we check whether any identified region contradicts the identified laterality and remove them. We then either split or merge bilateral versions of the same region.
are more precise than
Graph Construction Based on the matched regions, we associate bounding boxes with the observa- tions available. Additionally, we build a tree of all mentioned regions and fill missing intermediate regions based on the reference data. This allows to build graph of region nodes relevant the study.
We construct region region.regioon region wwwooonrelaiioni based located.at_rel retatiooe are constructed based on the regions attribute of observa- tions (direct specified). Additionally, we infer locatedaat regions (sub region) and bilateral versions of regions (bilateral). obs relat velations are constructed based on the parent-child structure of observations and their child type, as predicted by the LLM. abs_sent.relatinn derived from.
the reference data alone.
relations for sub
are constructed based on the sentences each observation was
Finally, we attach the indication information extracted from the report. Therefore, we build an additional observation node based on the L.-M-extracted answer for indi cation and the I.LM-xttractdd associated sentences, from which we can infer the associated observations and can infer all relevant tags.
E.2 QUESTION-ANSWER GENERATION
E.2.1 TEMPLATE ENGINE
To construct QA-pairs, we develop a template-engine that considers the information in the scene graphs to construct the answers. The template engine generates QA-pair by running the following steps:
1. Filter observations and studies based on the template configuration, e.g. only keeping observations of specific sub-categories.
2. Run a QA-strategy (indication, study abnormality, region abnormality, or finding) on the remaining scene graph. The strategy provides multiple named subsets of observations, variables to fill the template, as well as an overall state consisting multiple tags (c.g. is the study positive, are there any devices,...
3. Construct the template-bart based main answer by selecting and filling the answer-template based the state returned by the QA-strategy and the returned variables. Tags and bounding boxes can be inferred from defined observation subsets. (Not all templates provide such main answers)
4. Pick observation subsets identified by the QA-strategy and convert the observations into answer parts. The template configuration defines which subsets are picked and how they ordered. Additionally, template-based prefix- or fallback-answer. can be defined for each subset. Some subsets also be excluded based the QA-strategy state. These answers can be main-answers, details, or related information as defined in the configuration.
Additionally, the template engine supports variables, i.c. each template can be used to generate multiple QA-pairs. Variables can either be defined as lists (configured in the template) can be
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provided by the Qt-strategy (which might infer variables from the current scene graph, e.g. all mentioned regions). The question may then also contain such template variables.
E.2.2 STRATEGIES AND TEMPLATES
Indication this strategy, we use the extracted indication (if available) as the question. More precisely, use the indicat text. The main-answer constructed from the indication observation (i.e. the answer to the indication based the finding sentences), while detail answer parts constructed based on all associated finding observations. We include this question, if an indication observation is present in the scene graph.
attribute from the indication node as the question
Study abnormality In this strategy, we generate questions about abnormalities. This includes descriptions of the full study or specific categories of observations (e.g. devices), description of only abnormal findings, and yes/no questions of whether there are positive findings (overall or of specific categories) present the study. We use the templates defined Tab.
The strategy identifies five types of observations: (i) finding (positive), (ii) finding (negative), (iii) device (positive), (iv) device (negative). (v) acquisition. Based on the specific template, these are selected main-answers, details, related information. Additionally, template answer can be included, which is selected based on whether the study is abnormal or not. Some templates use different subcategories as variables, i.e. question is generated for each of the defined subcategories, where observations are filtered based on this subcategory.
Table 13: Study abnormality templates.
Template (ID)
Question Example
Varialles
Main neswer
Details
Related Inf.
finding (positve)
describe_all (00 Lewwar beeul]
Describe the gives study.
device (negative)
device (positive)
finding (negative)
acquistion
deseribe abnormal
Descaihe abnormal find- ings in given stady.
finding (positive)
device (positive)
802_tleccri www.rrrww.eeee..e... beusbnooraa
abnormal
Are there myy abnormal find--.
template finding (positive)
finding (negative)
device (positive) finding (negative)
303.i aaaaaooo mail
nanumnal
finding (pusilive)
finding (negative) dovice (povitive)
40-411 isnoo
stody normall?
taspllate
finding (negurive)
describe
finding (pusitive) finding (negative)
Evalusee the candiac strec- subeategory tures.
60 Budles los uboat.
deseribe abrormal subeat IV0 sew_aannrmat subbott
Descaibe pulmonary dis- subeanegory finding (positive)
disarders suggested by the study.
abnornal subeet is. h..ummal.ssber
Are there fractures subcategory finding (positive) diveases apparent the study?
templatt
linding (negative) linding (negative)
IV1 1 munrama Jubbott
Are the mediastinal and hilar subcategory contours normal?
finding (positive)
template
finding (negutive) finding (negative)
describe
(positive)
Check presence and posi- subeategory device (negutive)
$12.dwe de Losa Low
tion of devices, tubes, lines. and other foreign objects.
hosulesioes
template
B1 haa deolices
Are there any signs prior subcatogory device (positive) surpical procedures?
device (negutive) device (negutive)
deseribe acquisitoo B1 Gides it...oouiiioioon
Assess the image quality and sepects related age acquisitio..
acquisition
8115 descri be imagi ing art fecte
describe www.ing..aaaaa... artifacts
Describe apparent imag- artifacts and imaging. relited shadows.
aoquisition
his imaiiiigingg antifacts
Are there any imaging anii- facts imnging-rhhtted shud-
lemplate acgusstion
B1 6.a.. www..ii...n............. ifccca
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Region abnormality In this strategy, we generate question about anatomical regions. This includes describing regions, answering yes/no questions about the abnormality of regions, or describing specific aspects of regions (e.g. devices). We use the templates defined in Tab. 14
For a given region, the strategy first identifies observations associated with that region and classifies them into the five types defined in the study abnormality strategy. Additionally, it identifies observa- tions in related regions. This includes positive findings in parent regions or the opposite laterality. Additionally, a template answer can be included, which is selected based on whether the region is abnormal or not.
Before generating QA-pairs, the strategy first identifies set of regions. For each of these regions an individual QA-pair is generated. The set of regions is computed as follows: We always include a set of pre-defined default regions (the lungs, the heart, ...) and include all regions explicitly mentioned in observations, as well as their parent regions. Additionally, we randomly sample regions. Their sampling probabilities are computed based on how often they are associated with positive vs. negative findings, i.e. the more often region is associated with positive findings and the less often is associated with negative findings, the more often we sample it as a question. This assures that we generate additional negative questions for regions that are only/mostly mentioned with positive findings.
Table 14: Region templates.
Template (ID)
Question Example
Variables
Main answer
Details
Related Inf.
finding (pesitive)
201 describbe be .eeggioo on
descithe_egion
Deseribe the left lung.
region
finding (segative)
device (positive)
related regions
device (negative)
desuribe wtt.bbaaaooomm....oo region C02 seseereeee lbs cabnozmal.regg
Deserihe all aitoomal find- region ings in the lung bases.
finding (positive)
devize (pusitive) related regions
abnomnal region
templtee
device (posirive) funding (negative)
2.BBBla uelanaznn Lon
ings in the mediastinum?
Are there any absormal find- region
funding (positive)
related regions
CD4 L Lregion
is Humormal rregion
Is the heart normal?
region
tinding (positive)
template
region (positive)
finding (negutiiee) related regions
Co7 jlases als Lon, Lon, Low
descrihe_egion_deee
Check the right chest for subcunegory plants.
region
device (positive)
device (negative)
related regions
CDE www.arrggoonteriii...o.
desiree
Are there any tubes, limes, region subcategory
template
device (positive)
devioe (negative)
device (negarive) related regions
nete the left lung?
Finding In this strategy, we generate question about specific findings (radiological findings, diseaes, devices.... This includes descriptions of findings, yes/no questions about the presence of findings,
location of findings, and severity of findings. We use the templates defined in Tab. 15]
For a given finding/device entity, the strategy first identifies observations associated with it and classifies them into the five types defined in the study abnormality strategy. Additionally, it identifies observations that contain related finding/device entities. This includes parent findings (i.e. findings that are parents of the current one), same subeat findings (i.e. findings having the same sub-category). correlated findings (based on statistics computed over the whole scene graph dataset), indications of the current finding, and findings that are indicative of the current finding. The observation subset can be selected based on the template configuration. Additionally, template answer can be included, which is selected based on whether the finding is present or not and based on severity levels. This template may also be filled with information about the localization of the finding.
Before generating QA-pairs, the strategy first identifies a set of finding/device entities. For each of these entities an individual QA-pair is generated. The set of entities is computed as follows: We always include a set of pre-defined default entities and include all entities explicitly mentioned in observations, well their parent entities. Additionally, we randomly sample entities. Their sampling probabilities are computed based on how often they are mentioned positively vs. negatively (over all scene graphs), i.e. the more often finding is mentioned positively and the less often it is mentioned negatively, the more often we sample it as a question. This assures that we generate additional negative questions for findings that are only/mostly mentioned positively.
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Table 15: Finding templates.
Template
Question Exaeople
Virisbles Main answer
Datails
Related Inf
indication:
describe. describe.inding Do Leeeaci beeecribeefiii loeutnnd nndiin
finding (posiiioe)
Deserihe the pleural offusion.
finding
finding (Negativv)
indicative of
same subeat correlated
findings indications
heeffnding 2f ndll ng
finding (posiciv))
there any pacu- findling monia?
tomplate
finding (negative)
same sibeat
vorrebsed
D03 whezo 1a rinding
where..s.findnng
Where is the lung nodule lo- finding caledl?
templase
funding (positive)
finding (negative)
parent findings indications
D0 www.sewere...oooooooooooo... oe wtt.rrrriiiiiiiiooo.... sinding
www.sseverr.i.fiidiiee
severe diomepaly?
car- finding
tomplate
iindin (positive) finding (negative)
parent findings indications
describe_devee device
Describe the endotracheal device
device (posiiiv)) device (negative)
parent tindings subcat
dos cricribe devicc
hasdeviie
a pacemiker visible the device stady?
template
device (positive) device (negative)
same sulseat
b06.has.device
where.is.device
surgical clips device
template
device (positive) (negative)
subcat
www.ee devivicc
located?
E.3 QUALITY ASSESSMENT
Scene Graph Quality
The scene graph quality aspects are computed by simply inspecting the observations nodes and checking which fields are empty. Additionally, we track issues during the graph construction procedure and derive quality aspects from them.
QA Quality
We automatically assess the quality of the textual content of QA-pairs using Llama 3.1 8B (Gattafforii et al. 2024) as judge for the five criteria presented in Tab. 12
For rating entailment (Listing we condition the model on the report, the question, well as the answer parts and we rate each answer part individually.
Listing 7: LLM prompt used for entailment evaluation of generated answers.
You will be given Report (medical report of chest X-ray study), Question (about the study), and Answer (to the question) consiting of several (numbered) sentences.
Your task is to assess/rato whether each of the answer sentences is true not, given the reference report about the chest X-ray, This task known as entailment verification.
Assess the quality of each answer sentence independently and use one of
+ the rating options provided below to assess how well the facts in each sentences align with the report.
Guidelines:
Rate each sentence in the Answer individually; do NOT use any prior
+ answer sentences as context or source
Frovide the rating for each answer sentences in its own line starting
with the sentence number followed by your rating
For each sentence, use ONE of the rating options provided below, do NOT
+ use any other options
An example format will be provided
DO NOT REPEAT the question
sentences your response!
## Rating Options - ONLY USE ONE OF THE FOLLOWING OPTIONS
ALIGNED MENTIONED: Answer aligns with the report (is factually correct)
+ and all facta explicitly stated in the report.
kxample: The finding is described in the answer and the report
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ALIGNED INFERABLE: Answer aligns with the report (is factuslly correct) but some facts are NOT explicitly stated the zeport, can however be derived from what is written there.
Example: The answer provides more general description of what is 4 written in the report.
BORTTTVEENTT__TEO NOT_MENTIONED: Answer does NOT contradict the report may factually correct) but some facts (negative findings) cannot derived from the report, are however likely correct because they are negative findings and notning contradictory is mentioned in the report.
Example: The answer mentions that finding
present but this is not explicitly mentioned and does not contradict anything in the report.
ALIGNED GENERAL_ STATEMENT: Answer is a more general statement summary that is not explicitly mentioned but aligns roughly with the overall report.
Example: Summaries of whether the study is positive or negative, BOOLAALIGNDDLONN_IFEERRBEEE Answer does NOT contradict the report but the correctneaa of facts cannot be validated using the report. Example: The
mentions that finding present but this is never mentioned the report and cannot be concluded from it. NON_AAIIGEDD_ MINGDILMMSSLAAIING: Answer does NOT di rectly contradict. the report + out the description is highly misleading considering the report. mentions that finding is not present, which is never mentioned the zeport but could likely be present considering the report. LLLLALLNNDDCCTTRRAAI
Example: The
Answer contradict with the report. Example: The answer describes that a finding is not present, which is however mentioned present in the report or vice versa.
Example Format:
Report:
START REPORT
END OF REPORT
Question:
Answer (2 sentences to rate):
[01] First answer sentence.
[02] Second answer (last sentence in this example).
Rating (provide ratings):
[01] MENTIONDE
L [02] NON NON_ALIGNED_NOE NON INFERABLE
Proceed with the following Report, Question, and Answer sentences:
Report:
START OF REPORT
REPORT>
END OF REPORT
Question: <QUEST>
Answer KNUMANS> sentences to rate):
<ANSWERS>
Rating (provide <NUMANS> rat ings))
For rating relevance (Listing 8 we condition the model on the question as well as the answer parts (but not on the report) and we rate each answer part individually.
Listing 8: LLM prompt used for relevance evaluation of generated answers.
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You will be given question (about a chest X-ray study), and an answer 4 to the question) consiting of several (numbered) sentences.
Your task is assess/rate whether each of the answer sentences relevant answer the question or redundant. Assess the quality of each answer sentence and use one the rating 4 options provided below.
# Guidelines:
Rate each sentence the answer individually; but check for redundancy
+ with previcus sentences.
Provide the rating for each answer sentences with the sentence nunber
+ followed by your rating.
For each sentence,
of the rating options provided ded below, NOT use any other options.
An example format will be provided.
DO NOT REPEAT the question answer sentences in your response!
## Rating Options ONLY USE ONE OF THE FOLLOWING OPTIONS SOLEVANN_MMAINAANAAA
Fullfill ALL of the following
a) Are relevant to the questicn
b) Are needed to answer the question provide details
c) Are not redundant
previcus RELEVANT ANSSER sentences
RELATED_INES INFO: Fullfill ALL of the following
a) Are NOT needed to answer the question
b) Provide additional context related to the question or other answer sentences c) Are not redundant any previous sentences RHDUNDANT_INFO(.... Pullfill ALL of the following
a) Would fullfill criteria a-b) for RELEVANT MAIN ANSWER, Or
< RELATED INFO
b) Contains exactly the previcus sentence of the same type (ONLY consider previous sentences here!)
information that already provided in
c) Docs not provide any additicnal details or related information
d) Could be removed without changing the content of the answer Note: replace ... with the sentences IDs OF PREVIOUS SENTENCE with 4 which the current sentence TRRELEVANT_INFO: Fullfill ALL of the following a) Does not classify any of the above
redundant
b) No information in the sentence is relevant related to the questicn
Example Format:
Question:
Answer (4 sentences to rate):
[01] First answer sentence.
[02] Second answer.
[03] Third answer, containint additional information, everything already mentiened in 01 and 02.
[04] Fourth sentence.
Rating (provide 4 ratings):
[01] RELEVANT MAIN ANSWER
[02] IRRELEVANT INFO
[03] REDUNDANT INFO
[04] RELATED_INFO
Proceed with the following Question and Answer sentences: Question: <QUEST>
Answer NUMANS> sentences to rate):
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<NSWERS>
Rating (provide <NUMANS> ratings):
For rating completeness (Listing we condition the model on the report, the question, as well as the full answer and we rate the full answer whole.
Listing 9: LLM prompt used for completeness evaluation of generated answers.
You will be given Report (medical report of chest X-ray study),
Question (about the study), and an Answer (to the question)
Your task is to assess/rate whether the provided Answer centains all the the Question, considering the Report as the source of truth.
necessary information to
# Guidelines:
Do not assess whether the answer is correct but whether it is contains all relevant information from the Report to the Question.
Use ONE the rating options provided below, do NOT use any other
+ options.
Answer with short explanatien (a few words) followed by "->" and the rating optien.
example format will provided.
DO NOT REPEAT the report, question, or answer sentences in your 4 response!
## Rating Options ONLY USE ONE OF THE FOLLOWING OPTIONS
FUULY_CCOMPLS COMPLETE: Al1 facts from the report that are relevant to the FULLY
+ question are included and the question is answered.
DETAILS_MISINGG The main facts from the report that are relevant to the questien are included BUT
details are missing.
NOT_ANSWERED: While facts from the report may contained, the 4 does not relate to the question at all.
INCOMPLETE_NOM SOFF__NO__MTT JIMMPEETTELONLMMSLAAAINN: Main facts are missing, but these should not 4 lead to mistepresentation of the facts (e.g. only some negative not mentioned).
*iindings
INCOMPLETE MISEEAA ING: Important facts are missing, such that the + answer may mislead the reader.
# Example Format:
Report:
START REPORT
END OF REPORT
Question:
Answer (to rate):
Rating (your task):
severity is missing -> DETAILS MISSING
Proceed with the following Report, Question, and Answer:
Report:
START OF REPORT
REPORT>
END OF REPORT
Question: <QUEST>
Answer (to rate):
<NNWERSS>
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Rating (your task)
For rating question clarity (Listing 10 condition the model the question only and rate it.
Listing 10: LLM prompt used for question clarity evaluation of generated questions.
You will be given medical Questien about radiological chest X-ray net provided).
study (which
Your task is to assess/rate the clarity of the Question, i.e. whether its
+ wording is clear and unambiguous, and whether it is easy to understand and answer.
Guidelines:
Use ONE of the rating options provided below, do NOT use any other
+ options
Answer with short explanation few words) followed by "->" and the
rating opticn
An example format will be provided
DO NOT REPEAT any part of the question or answer sentences in your response!
## Rating Options ONLY USE ONE THE FOLLOWING OPTIONS
OPTIMAL: The question is mostly clear, unambiguous, and can be answered
+ It is well-structures and cencise without granmatical errors. SENGGALL_SETTEEESSSTRRRICSS
The question is mostly clear, unambiguous, + and can answered. However, the sentence structure is unusual or 4 complex. There are no grammatical errors.
GRAMAATICAL ERRORS: The question is mostly clear, unanbiguous, and can + be answered. However, there are grammatical errors that may affect the clarity. The sentence may may not be well-structured.
WNRELATED_TO_CHE CHEST_XRAY: XRAY: The question is mostly clear and unambiguous. + However, it does not make sense to ask this question about a chest + X-ray study, because it does not relate to the content that can be observed in chest X-ray. There may may not be granmatical errors or unusual sentence structure.
UNCLBAR BEEELARRQQEETTINNS QUESTION: The question may be misunderstood, is ambiguous, or
+ otherwise unclear. Any answer could be misleading or incorrect,
+ even with proper medical knowledge and context. There may may not grammatical errors unusual sentence structure.
Note that simply stating the indication/history motivating the study is
+ considered a valid question (and should not be rated as ENCLEAR_QQESTER solely for not being explicit question)!
Example Format:
Question (to rate):
Rating (your task):
The question is unrelated to chest X-rays -> UNRELATED CHLTT XRAY
# Proceed with the following Question:
Question (to rate): <QUEST>
Rating (your task):
For rating answer clarity (Listing [T], we condition the answer parts only (but not on the report or question) and rate each answer part individually.
Listing 1I: LLM prompt used for answer clarity evaluation of generated answers.
You wi11 be given medi cal Answer to unknown question about radiologica chest. X-ray study (which is not provided).
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Your task is assess/rate the clarity of each sentence of the Answer, ... whether wording clear and unambiguous, whether easy to understand.
# Guidelines:
Rate each sentence in the Answer individually; do NOT any prior
4 answer sentences as context or source
Frovice the rating for each answer sentences in its own line starting
+ with the sentence number followed by your rating
For each sentence,
ONE the rating options provided below, NOT use any other options
An example format will be provided
po NOT REPEAT the question or answer sentences in your response!
## Rating Options ONLY USE ONE OF THE FOLLOWING OPTIONS
OPTIMAL: The answer sentence is mostly clear and unambiguous.
+ #cll-struuturrc and concise without grammatical errors. MUUSIALLSENTTNNEESSTTUUTOT
The answer sentence is mostly clear and unambiguous. However, the sentence structure is unusual or complex. There are no grammatical errors.
GRAMMATICAL_ERRORS, The answer sentence is mostly clear and unambi unambiguous guous + However, there are (severe) grammatical errors that affect the 4 clarity. The sentence may may not be well-structured.
COCLEAR_ANSKER, The answer sentence may misunderstood, is ambiguous, or otherwise unclear. There may or may not be granmatical errors 4 or unusual sentence structure.
NOT_UNDERSTANDABLE: The answer sentence cannot be understood at all. It is completely unclear, nonsensical, gibberish, or contradictory in itself. There may may not be grammatical errors unusual sentence structure.
# Example Format:
Answer sentences to rate):
[01] This first sentence.
[02] This the second answer sentence.
[03] Some text where it is unclear what is meant.
[04] This is the last answer sentence.
Rating (provide 4 ratings):
[01] GRAMMATICAL ERRORS
[02] OPTIMAL
[03] UNCLEARLANSWER
[04] OPTIMAL
# Proceed with the following Answer sentenees:
Answer KNUMANS> sentences rate):
<ANSEERS>
Rating (provide <NUMANS> ratings):
E.4 RESOURCES FOR DATASET CONSTRUCTION AND EVALUATION
E.4.1 SOURCE DATASETS
MIMIC-CXR (Johnson et al. 2024b: 2019a Goldberger et 2000) We use the MIMIC-CXR
dataset version 2.1.0 (attps:/ phy si ooiione....m org cont ent /tt//mmimmi/////////ii/miiiieiit cxx / 2 1. 0/ as the source of radiology reports from which extract the scene graphs. It contains 227 835 radiographic (chest X-ray) studies performed at the Beth Israel Deaconess Medical Center Boston, MA, USA. licensed under the PhysioNet Credentialed Health Data License 1.5.0.
MIMIC-CXR-IPG (Johnson et al. 2024a 2019h; Goldberger et al., 2000) We use the MIMIC- exr- /
CXR-JPG dataset version 2.1.0 https: /ttp::///hysiiomoto http:/hyysooooe/e wwt.://ttt..orctotonnnnoooccottt
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2.1.0/) the source of images for localization (the CXAS segmntation model is applied on these images). Additionally, we use the provided radiologist annotations (mimic-cxr- te--0-test-set-tae
csv) as targets to evaluate the quality of extracted finding tags (Tabs. and 7). The dataset is derived from MIMIC-XRR and licensed under the PhysioNet Credentialed Health Data License 1.5.0.
Chest ImaGenome [Wu al. 2021a Goldberger et al. 2000) We use the Chest ImaGenome
/ 0 07) as source anatomical region bounding boxes for localization. Additionally, we use their provided scene graphs bascline for the evaluations of our scene graphs (Tabs. La and to I0). It contains scene graphs for 242 072 frontal images from MIMIC-CXR that have been created using rule-based natural language processing and CXR atlas-based bounding box detection. The dataset is derived from MIMIC-CXR and licensed under the PhysioNet Credentialed Health Data License 1.5.0.
Dataset version 1.0.0 (https: :
/ttpss///hhyas /phys ionet. org/ content. hwtpeett/chettteeeeee wwt.sthhtsttteeeeeeeeeeeeee
Goldberger et al. 2000) We hittps:/pphysinneee org orncootenttttttttteoteeeoomo content/ targets evaluate the quality of ex- tracted finding tags (Tabs. [a and [). More precisely, we use the gold standard dataset provided for Task in the CXR-LT 2024 challenge tasks (406 reports, 26 classes). The dataset derived from small subset of MIMIC-XR and was hand-labeled by radiologists. It is licensed under the PhysioNet Credentialed Health Data License 1.5.0.
CXR-LT 2024 (Holste et al., 2025; 2024;
the CXR-LT 2024 dataset version 2.0.0
1o-----ice--workhooe wworkshop-cvamd/22 wwwnd/2.0.0...../ 0.0. 07)
MS-CXR (Boecking et al. 2024 2022; Goldberger et al. 2000) We use the MS-CXR dataset 0/) targets to evalu- ate the quality of extracted finding boxes (Tabs, 2b and 9 The dataset contains 1162 image-sentence
version 1.1.0 (bttps:t::: http:phyysiicc orgg cont ent eeaiitttemmmmstee http://scx//////
pairs of bounding boxes and corresponding phrases (and their finding classes) for 8 different findings. is derived from a small subset of MIMIC-CXR and was hand-labeled by radiologists. It is licensed under the PhysioNet Credentialed Health Data License 1.5.0.
REFLACX (Bigolin Lanfredi et al. 2021
2022; Goldberger et al.) 2000) We
https://phys.: http//hyssiooo onet. org content/ 0.000.07 07) as targets to evaluate the quality extracted finding boxes (Tabs. and I0). The dataset provides eye-tracking data collected for 3032 frontal chest x-rays from the MIMIC-CRR dataset. Additionally, it provides hand-labeled ellipses localizing for several anomalies present in the images. We only use the ellipses but do not use the eye-tracking data. licensed under the PhysioNet Credentialed Health Data License 1.5.0.
use the REFLACX dataset version 1.0.0
https:
reflacx -xFlacx-xray---e lowayooocllzztiino
E.4.2 MODELS
LLM Selection
We choose the information extraction model (Llama 3.1 70B) based the follow-
ing criteria:
Open weights and locally hostable (required due to the MIMIC dataset licenses).
Fast and efficient to run on large amounts of data.
Sufficient extraction quality and instruction following. Sophisticated reasoning capabilities are not required.
No vision component is required.
While using other models would be possible, choose Llama 3.1 70B based preliminary experiments (at the start dataset construction). Those experiments indicated that provides good trade-offs for the points above.
The study of alternative models may be interesting. However, we do not expect major improvements in data quality, a robust framework to assure data quality even with small data extraction errors. Therefore, we leave this to future work.
semantic entity mapping and our automatic quality assessment already provide
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Llama 3.1 70B (Grattafiori et al. 2024) We use the AWQ-INT4 (Lin et al. 2024) quantized version of Llama 3.1 70B Instruct provided by the Huggingface hub at https::///.........p http::///gggggggggggeggg co/ nugging-quante
- Ints----stuuutttt---- AWO- INT4 The model is derived from the httehttp::/// http:hhhggggggggeccccoo cttcco//eeeeee a amaa 3...aa...aaaa...1-1........333 1:08-Instrus rustrrut and is licensed under the LLAAMA 3.1 COMMUNITY LICENSE AGREMMENT. We limit the maximum number of tokens to 6144.
Llama 3.1 8B Grratafforri et al., 2024) We use the AWQ-INT4 (Lin et al. 2024) quantized version of Llama 3.1 70B provided by the Huggingface hub at ht hugg wgafacc Co/ hugging - from the https: htt///hhgggnngagggeeeo /
Llama- - 8B Instruct - AWQ INT4 The model is derived co meta aama/Llana ama 1 8B Instruct and is licensed under the LLAMA 3.1 COMMUNITY LICENSE AGREEMENT. We limit the maximum
number of tokens to 8192.
CXAS (Seibold et al. 2022 2023) We use the model provided by the CXAS Python
library hctps://pyes org pro ject cxas/ See also https://githut com/ ConstantiinSibollee Chestt/Chhhtctec wwt.eeaaaaaayayyeaaaaaaaaa
licensed under the SatirttoonnoooCCmmmerria-------- Alike 4.0 International license. We run segmentation of all anatom- ical structures on half the original image resolution (half original image width and height).
BioLORD (Remy et al., 2024) We use the BioLORD-2023-C variant provided by the Hug- http:://hhgggnggg
gingface model hub at and licensed under the MIT license. To apply the model, we use the Sentence Transformers library /https://gitii under the Apache-2.0 license.
co TreeyCoomaan//iooORR 2023-C
which is licensed
For all LLM-based information extraction steps, we rely on the vLLM library (Kwon al., 2023) (https: http//gttubb..t wwwpwwwwwwwwet..t..........wwelt propimm/mioojot//iimml Apache-2.0 li- cense) for inference. We run all models with temperature = 0.0. All json-outputs are parsed using the Pydantic libary (httts: :tttps://docs htt/docsspyddan.icc pydantic.des.
Model Inference Details
E.4.3 COMPUTATIONAL COSTS
Each dataset construction step can run on an individual Nvidia A100 GPU, but we use multiple GPUs in parallel, with each GPU responsible for different subset of the dataset. Semantic segmentation of all 158 anatomical structures using the CXAS models takes about seconds per image, leading to total of about 628 GPU hours. Sentence extraction takes about I second per study (report), leading to a total of about 65 GPU hours (for 227 835 studies). Observation extraction takes about 1.7 seconds per study, leading study, leading to a total of about 19 GPU hours. Scene graph construction (including entity matching) takes about 0.6 seconds per study, leading to tool of about 38 hours. Question-answer generation does not require a GPU but takes about 9 seconds per study (including all question templates and strategies), leading
total of about 108 GPU hours. Indication extraction takes about 0.3 seconds per
total of about 24 days. However, multiple processes can be run parallel on a single machine, leading to an effective time of only about a day for all 42M QA-pairs. Quality assessment of QA texts again requires a GPU and consists of individual steps that can be run in parallel. Overall the assessment takes about 6 GPU days for all 42M QA-pairs.
Societal Impact As large vision-language dataset for medical imaging, this dataset has significant potential for societal impact. However, its use as training source for models employed in clinical or medical applications also poses substantial risk of misdiagnosis, highlighting the need for caution. Therefore, we strongly advise against relying solely on this dataset for fine-tuning or evaluating such models. On the other hand, this dataset can facilitate the development of large and interactive VQA models, which can provide supplemental information for patients, serve as a training tool for healthcare professionals, optimize clinical workflows. The provided annotations, including bounding boxes and tags, further enhance its utility by providing a level of transparency and explainability in model predictions, allowing for more informed interpretation and analysis. By sparking research in this direction, this dataset can contribute to the advancement of the field and ultimately lead to positive long-term societal impacts. Nevertheless, it is essential to approach this
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dataset with caution, recognizing its limitations and potential risks if used improperly. As such, consider this dataset a valuable research asset, but not yet suitable as a (sole) training source for real-world medical applications, emphasizing the need for careful evaluation and validation.
F STRUCTURED VQA TASK
F.1 FURTHER STRUCTURED VQA RESULTS
We show all the metrics from Tab. 4 with additional sub-metrics of our RadStruc metric, each with 95% confidence intervals (bootstrapping, n 1000). Besides our default VQA models (trained on different CXR-QBA subsets) and the baselines, namely training without bounding boxes and/or tags and predicting bounding boxes and tags before after the text. Apart from these adaptions, the experimental setup was the same as in Sec. 5 We found that none of these adaptions has major influences on the results (apart from being capable of predicting boxes/tags), indicating that text, boxes, and tags our dataset do not contradict each other. However, we observed minor improvements in text quality by adding bounding boxes
Table 16: Further results our structured VQA task from Sec. 5
also show alternative settings of our VQA model (each trained on the FT subset),
(ablations)
(default)
Qwen3-VL
CXR-QRA Set
FTUMM)
FT/IM)
FT(M)
FTUM)
FT(IM)
PTYM)
PT(M)) PT(M)-FFRIM)
x
Hotes
Tags
X
Box/Tag Position
after test
before text after test
before text after test afler text
after text
after text
after text
afler test
Logical
0.76
0.76
0.67
0.68
0.25
Logical Rec.
0.74
0.75
0.75
0.75
0.75
0.69
0,70
0.77
0.64
0.58
0.08
Logical F]
0.74
0.75
0.75
0.75
0.70
0.71
0.27
0.34
RodFact
Grounding
0.88
Grounding Roc.
0.88
0.90
0.88
0.89
0.92
0.92
0.90
0.12
0.51
Grounding P1
0.85
0.83
0.83
0.83
0.85
0.66
0.85
0.32
0.37
Sputial
0.67
Spucsal Rec.
0.67
0.68
0.68
0.68
0.64
0.65
0.70
0.07
0.31
Spucial Fl
0.63
0.63
0.64
0.63
0.60
0.62
0.67
0.06
0.23
Finding Prec.
0.68
0.67
0.68
Finding Rec.
0.67
0.66
0.61
0.62
0.68
Finding
0.67
0.69
Hinding-per
Finding-pos Rec.
0.29
0.26
0.13
0.16
0.31
Finding pos
0.39
0.39
0.36
0.40
0.43
Region
Region Rec.
0.66
0.66
0.62
0.63
0.68
Region Fl
0.66
0.67
0.64
0.65
0.69
Region-pos Proc.
RoSSineNQQ (Ta-o)
Region pos
0.12
Region-ppes Fi
0.29
0.32
0.27
0.30
0.36
Main-culegury Prec
0.73
0.73
0.66
0.67
0.75
Main-conegory Roc.
Main-cntegory
0.72
0.72
0.68
0.70
0.74
Main categury pas Prec.
0.49
0.52
0.39
0.43
0.55
Main-cutegory pos
Main-colegury
0.47
0.19
0.42
0.45
0.52
Sul-categoey Pree
0.71
0.71
0.69
0.76
0.73
Sub-category Roc.
Sub-cutegory
Sub categosy Prec.
0.47
0.50
0.37
0.41
0.53
Sob cale-caaeory-ppoe pos Rec.
0.32
0.15
0.18
0.36
Sub--onegoey-poss
Bhon-pos-eniyy Prec.
0.32
0.25
0.27
0.36
Bhan-pos-emity Rec.
0.22
0.20
0.11
0.13
0.25
Bbox pos eniity
YOur KadStruc implementation.
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F.2 RADSTRUCVQA METRIC
Table 17: Comparison of different LLMs for entailment prediction in the RadStructVQA metric. We compute the Peasron correlation over the per-sample scores our default model, computed using different entailment LLMs. We found almost perfect correlation between the two larger models (right column), despite them being from different families. This indicates that the Llama judges do not prefer their predictions (as Llama trained model. Even between the small Llama 3.1 8B and the other models we find high correlation, such that serves reliable part of the RadStruc metric.
both used during dataset construction and as part of the
Pearson Correlation
Llama 8B Llama 70B
Llama 3.1 Qwen3-328
Llama 3.1 70B
Qwen3 32B
Logical Prec.
0.90
0.94
0.98
Logical Rec.
0.90
0.90
0.97
Grounding Prec.
0.97
0.99
0.99
Grounding Rec.
0.96
0.99
1,00
Finding Prec.
0.96
0.98
0.99
Finding
Finding pas Prec.
Finding-pos Roc.
0.90
0.92
0.96
Region Prec.
0.97
0.98
0.98
Region Rec.
0.97
0.97
0.98
Following the RadFact (Bannur et al. 2024) metric, we split the predictions into individual elements. In our case, we treat each answer part as its own element, ignoring the hierarchy level and order. For each QA-sample, this results in a set of prediction elements D, where | is the number of answer parts the predicted answer, and a set of target elements y, where y is the number answer parts the target answer.
For each VQ sub-metric (sub E {logical, grounding, finding.... ......)) }), we compute sample- level precision Psub and recall rsub score individually:
Paub ().))
Ssub (})2)).
Tsub (5.3) Ssub (2)
where Saub (H,C) [0, is sub-metric specific scoring function considering the hypothesis set H. given the context set C. For precision - the prediction set and C = y is the target set, while for recall H. y and y.
The score Ssub is computed as the fraction of relevant hypothesis elements h E H that are entailed, using a sub-metric specific entailment definition, given the context C. More precisely:
n E 76 entailedgub C(h) relovant,ub (h)
Ssub (H, C)
(3)
[n E 7 relevant,ub (h)
where Cl4] is the evidence from C for defined as
Cīh] = { c C h is logically entailed with C A provides evidence for f
(4)
We compute C[h] by prompting an L.M to (i) identify entailment of given all context elements in C, where h can be ENTAILED NOT.ENTAILED (neutral or contradicting): and (īi) provide the relevant evidence for entailment, i.c. the context units c E that support h. The LLM is given only the textual descriptions of each element (answer part), i.e. the entailment classification is purely logical and does not consider localization or any tags. Note that C(h] = O if not entailed.
Given the hypothesis h and its evidence C[h], the sub-metric entailment is computed individually by
entailedeab (h, C(hl) E {true, false).
(5)
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while the relevant subset of hypothesis elements is identified using the sub- sub-metric specific relevantsuu (ft E {true,
(6)
The definitions for each sub-metric can be found in Tab. [8]
Table 18: RadStrucVQA sub metric definitions. The logical, grounding, and spatial sub-metrics follow the same principles as the corresponding sub-metrics in RadFact (Bannur et al. 2024).
Sub-metric
entailedgut (N.)
relevant (h)
[[ is not emptyy
logical
Le there pusiiive evidence for h in does contradict C
always true
enttailediogio (h, CThl) [o, C(k)) 2 0.5 where JoHt the Intersection between boxes in is boxes C(Kk over the toral box areo in lt,
bounding
grounding
entailediogieal (h, C'A])
with interscction/aeen computed based hox-maeks (unions hoses)
spatial
entailed groundin (h, C(A])
h has bounding boxes
entallediogical C[N)
linding
each of the finding lags in h present in any of CTh), only considering the C[h] with same positivity
h has finding Lags
relevanteending (1) A is positive
finding-pos
entailedneding (h. ((A))
entaileriwuioal (h, C(A.) A
region
each of the region tags in h is present in of [[N].
h has region tags
only considering the subset of C[h] with the same positivity
region pos
entaileddegiion C(h)
relevantsesiian (h) A positive
entailediosieal (h, C(N)) A
main-calogory
each of finding main category tags present any C(A].A has finding main eategory tags only considering the subset of CTh) with the same pusitivity
relevantmain castegay (h) positive
main- cat--carteeeyyye pos
entailed category (h. (Tb))
entailedhogioal (h, C(hi) A
sub-category
each the finding sub category tags in present in any of C(h), A bas finding sub category tags only considering the subset of C[h] with the same pusitivity
sub-cate cab-categury-pos
eutailedduh--caregrr (h, (TA)
relevant catbggrryyrrrr (h)
h positive
yelevanlending-poo (h) relevantepatial (h)
bhox-pos-entity
entailednuddnng c(b)) enntaileeggrommdtneeA (h, C(N)
Implementation Details The final precision/recall scores are computed by averaging the sample- level scores. F1 scores can also be computed by first taking the per-sample harmonic mean of precision and recall before averaging the sample-level F1 scores. Invalid answers, samples with LLM parse errors during entailment computation, as well as samples without relevant hypotheses are ignored during averaging. We use the same entailment prompts and few-shot examples as in RadFact (Bannur al. 2024) but the Llama 3.1 8B (Grattafiori et al. 2024) model, allowing us to compute the metric locally.
F.3 EXPERIMENTAL SETUP
Vision-Langusa Model Training Our vision-language model follows the Llava architecture Liu et al. 2023), using Rad-DINO (Pérez-García et af. (025) (mi crosoott/addddinneeoo for image encoding and the 3B Llama 3.2 language model (https: httpt:::///gggggge hww.iiigwcc..ccoe/eeeeeeeeeeeeee woteeeeemeeeeaaaala..aa...
Llama-3 3. 2 3B Instruct) connected via an MLP projection layer. We freeze the image encoder and all existing language model parameters but add new special tokens (with trainable embeddings) and apply LoRA (Hu et al. 2021 to the language model. Therefore, we only train the projection layer, the LoRA parameters, and the newly added token embeddings (keeping the existing token embeddings frozen). We train for one epoch, either on 1M QA pairs randomly sampled from the fine-tuning (FT) grade set 1M QA pairs randomly sampled from the pre-training (PT) grade
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Published as a conference paper at ICLR 2026
set of our CXR-QU -BR dataset (MIMIC- train split). We use autoregressive training but only apply the loss to answer tokens. For image encoding and projection, we adopt the hyperparameters of MAIRA-2 (Bannrr al. 2024): We square-crop the images and resize them 518 518, leading to 37 x 37 = 1369 image patches (i.e. image tokens), then we use the features of the last image encoder layer, and project the image tokens using 4 projection layers with GeLU activations. For LoRA, we use = 64, a 16, and dropout 0.05. The maximum number of tokens for the language model is restricted 2048. We use the AdamW optimizer with cosine annealing scheduling with 500 warmup steps, maximum learning rate le - 3. no weight decay, a batch size of 4 with 16 accumulation steps. gradient norm clipping at 1.0, and bf precision. All models are evaluated on the test split (following MIMIC-CXR) of our CXR-QBA fine-tuning (FT) grade set.
Prompt and Special Tokens Our question prompt follows the template shown in Listing 12 where <boi> (begin of image), <eoi> (end of image), and < <imgre ff> (first image reference) are newly added special tokens, <img> tokens are replaced by image token features, and {QUESTION] is replaced by the specific question.
Listing 12: Question prompt.
Consićer the following chest X-ray image: <boi><digrref>>xinng.... + QUESTION)
<eoi>> {
The answers are formatted into sequences using XML-style structures and special tokens to represent
tags and bounding boxes. An example is given in Listing I3
Listing Answer prompt.
<answer>
<categories>
FTILIK//DDDDD--xxUSSEEPUUUUUOUO DISFABRS</subcat o </categories>
<subcat>1.NG
No, there is no indication of pneumothorax. </answer>
We use special start and end tokens for answer parts (Sanswer> </answer>), bounding boxes (<box> </box>), and groups of tags (<regions> ///rgiions,, <probability </probability", <categories> / < /attegrreees, <entities> </entities> smodifierse <///<///ofiii fiers>). For some tags use individual special tokens, namely for laterality (e.g. <bilateral>) regions (e.g. <lungs>), certainty (e.g. <certain>), positivity (e.g. <neg>), and main categories (e.g. <ANATOMMCALLFINDDIGGS tokens and normal text, namely for sub-categories (<subcat> / </ subbca>>) and finding entities (<entity> / < </entity>) Bounding boxes are listed after all other tags, where we use <box> / </box> tokens and refer back to the image using <imgrefi>, Inside the box-tokens we use special relative coordinate tokens (following MAIRA-2 (Bannur et al. 2024)) that represent the normalized (I1, 91. 22, 82) coordinates of the bounding box, each quantized to 100 different tokens per dimension. We use different tokens for the x- and y-dimensions but share them for both corners (e.g. and T2 share the same token set). The textual description is the last part of each answer part and consists of plain text without special tokens. If an answer consists of multiple answer parts, then each answer part close to the existing token embeddings, where we try to inttialize them based on keywords defined for each token. More precisely, given of keywords for using the old vocabulary and compute the average embedding of all these tokens. This is then used as the initialization for the new token.
For others
start/end
individual block in Listing [3] All new token embeddings are initialized
token, tokenize the keywords
MAIRA-2 Baseline We use the MIARA-2 (annur et al. 2024) checkpoint available at phttpp
//huggi ngface. co/mi wtw.oct ra 2 We freeze the full model but modify the prompt.
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Published as a conference paper at ICLR 2026
More precisely, asking the model to answer to the question (included in the modified prompt) instead of reporting all findings in the image. The rest of the prompt is kept unchanged. This model is then evaluated the same test set as our vision-language model. It is capable of generating individual answer parts, each with bounding boxes, but does not generate bounding boxes for negative answers and cannot generate any tags.
their original prompt for grounded report generation but slightly modify it,
Qwen3-VI. We use the 4B Instruct version of QWen3VI., using the checkpoint available at htttss://hugee wgw.ac.c..... co/ Qwen -V 4B- Inst ruct, We do not train the model but prepare prompt that asks the model provide individual answer sentences per finding and to provide textual bounding boxes. For this we also provide reference size of the image, based on which we evaluate the predicted bounding boxes, All image processing follows their implementation.
https::/huggii. wtwcce.......c.ceeeeeeeeaeaeaee. co/ chaoyinshe/ sava-med-vi 5- st ral 7b- such that the huggingface library can directly be used for inference. We do not train the model and use the same prompts for Qwen-VL. All image processing follows their implementation.
Llava-Med v1.5 We the checkpoint provided
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Reviews:
[{"review_id": "877p3S1m7t", "reviewer": "Reviewer_uagn", "summary": "The paper introduces CXR-QBA, a very large chest X-ray VQA dataset built from MIMIC-CXR that contains 42.2M QA pairs with full-sentence, multi-part answers, bounding boxes for grounding, and structured tags for findings and regions. The authors construct visually grounded scene graphs from radiology reports using LLM-based information extraction, semantic entity mapping, and region localization, then generate questions and answers from these graphs. They automatically rate QA quality with an LLM judge and release two subsets: pre-training grade (31.2M) and fine-tuning grade (7.5M). They also propose a structured VQA task that requires models to output text, boxes, and tags, and report a baseline LLaVA-style model with Rad-DINO and Llama-3.2-3B that outperforms an adapted MAIRA-2 reporting model on their new metric, RadStrucVQA.", "questions": "- Does the pre-training grade include only B-rated samples, or does it also include A and A+?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "- The dataset is orders of magnitude larger than prior CXR VQA resources and includes sentence-level answers, localization, and detailed tags.\n- The scene-graph construction is well described, including CXAS-based region masks, Chest ImaGenome boxes, LLM extraction, and BioLORD-based concept mapping, followed by LLM-as-judge quality assessment and graded splits.\n- The authors compare derived tags and boxes to radiologist gold standards and public annotations.\n- The structured VQA task and RadStrucVQA metric encourage outputs that are logically correct and visually grounded with tags.", "weaknesses": "- I am not sure how reliable the LLM-as-a-judge quality assessment is when the judge is Llama 3.1 8B. The paper should calibrate this with stronger judges and report robustness checks like cross-judge agreement.\n- There are plenty of recent multimodal LLMs that could serve as reference points. Incorporating strong open-source baselines such as Qwen3-VL or LLaVA-Med v1.5 would better situate this work relative to current progress.\n- Human–LLM agreement is only fair to moderate.\n- The authors mention co-design with radiology collaborators, but they do not clearly report the number of participants, and their roles are vaguely described.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "JQv9Ph7Cep", "reviewer": "Reviewer_V7HF", "summary": "This paper introduces MIMIC-Ext-CXR-QBA (CXR-QBA), a large-scale VQA dataset for CXRs, derived from the MIMIC-CXR dataset. It comprises 42 million automatically generated question-answer pairs with multi-part, multi-granular answers styled like radiology reports. Each study includes detailed annotations, such as bounding boxes and structured tags (findings, regions). The dataset was constructed using an automatic scene graph construction pipeline, which leverages LLMs, semantic entity mapping, and localization models. The authors also define a structured VQA task, propose evaluation metrics, and provide a baseline model to demonstrate the utility of the dataset and guide future research.", "questions": "1. Are bounding boxes derived from CXAS masks always higher in quality than those from Chest ImaGenome? Given that both sources are used in the dataset, what criteria determine when one is preferred over the other?\n2. What are the criteria for defining Parent Regions and Fusion Regions?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1. Introduces MIMIC-Ext-CXR-QBA, a large-scale CXR-VQA dataset with 42 million QA pairs.\n2. Provides rich and detailed answers in radiology report style, along with fine-grained annotations including bounding boxes and structured tags.\n3. Implements robust quality control, including validation against expert annotations and assessment of automatically generated outputs.\n4. Proposes a structured VQA task for CXRs and provides a baseline model to showcase dataset effectiveness and support further model development.", "weaknesses": "1. The dataset is automatically generated using large language models (LLMs). While the authors employ various quality control, there remains the potential for subtle errors or inconsistencies that may not be present in datasets fully curated by human experts. In particular, it is unclear whether the LLaMA-8B model provides sufficiently reliable quality assessment compared to larger, more capable LLMs.\n2. Since the dataset is derived from MIMIC-CXR, it likely inherits demographic and clinical biases inherent to that specific patient population, which may limit the generalizability of models trained on this dataset.\n3. The baseline model (MAIRA-2) is fundamentally a radiology report generation model, rather than a specialized VQA model or medical foundation model. This raises questions about the appropriateness of the baseline for evaluating performance on a VQA-specific task, as it may not accurately reflect the potential or limitations of models explicitly designed for structured question answering.\n4. The evaluation process relies on the LLaMA-8B model to determine entailment in the RadStrucVQA metric. Given the model’s relatively small size, there are reasonable concerns about the accuracy and robustness of its entailment judgments.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "GKYSFgIiTk", "reviewer": "Reviewer_TU8P", "summary": "This paper proposes CXR-QBA, a new large-scale VQA dataset for chest X-rays. Based on MIMIC-CXR, the authors employed a three-stage automated pipeline: (1) scene-graph construction (including an information extraction model, an embedding model, and localization models), (2) template-based QA generation, and (3) automated quality assessment (mainly for deciding whether to use samples for fine-tuning) using a judge model (Llama-3.1-8B). Through this pipeline, the authors generated a total of 42.2M QBA (question, bounding box, answer) pairs. The main contribution is richly detailed automatic annotations for MIMIC-CXR at large scale, which might be better than the Chest ImaGenome dataset.", "questions": "Q1 (Regarding the utility of “Grade B” data): \nThe “Grade B” (pre-training) data comprises 58.8% (24.8M) of the dataset, and proving its utility is essential. Could you provide an ablation study comparing the baseline model’s (Sec. 5) performance under these three conditions:\n  * (A) Fine-tuned on “Grade A” data only.\n  * (B) Pre-trained on “Grade B” data, then fine-tuned on “Grade A” data.\n  * (C) Pre-trained on the full (A+B+…) dataset, then fine-tuned on “Grade A” data.\n\nQ2 (Regarding the Validation of Scene Graph Accuracy): \nThe pipeline depends heavily on the Scene Graph Construction step, yet that step lacks independent validation. While benchmarks against MIMIC-CXR-JPG Test, CXR-LT, MS-CXR, and REFLACX are useful, they cover only overlapping, smaller scopes. Given the expanded coverage (257 regions, 221 findings), was a small internal gold standard (e.g., radiologist-annotated) created to validate factual accuracy of scene-graph outputs? If so, please report:\n- (a) Segmentation accuracy of the 158 CXAS masks (e.g., Dice).\n- (b) IE performance of Llama-3.1-70B (e.g., F1) against this internal gold standard.\n\nAdditionally, could you provide component-level benchmarking results against the public gold-standard datasets from RadGraph and Chest ImaGenome?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- The dataset’s scale is a massive contribution, with 42.2M pairs, including a 7.5M high-quality fine-tuning subset. The answer details are a key strength, providing rich, full-sentence responses derived from reports.\n- The three-stage automated pipeline is a significant contribution in itself.\n- The authors quantitatively benchmarked their scene graphs against the existing Chest ImaGenome dataset. They demonstrated that their pipeline produces superior results, notably a 20% performance improvement on long-tail (rare) disease classes, proving the value of their extraction method.\n- The paper’s presentation is excellent. It is clearly written, logically structured, and uses numerous figures and tables to effectively explain the complex pipeline, dataset, and results.", "weaknesses": "* While the paper claims “detailed” or “fine-grained” localization, its bounding boxes correspond to 257 broad anatomical structures, not the pathologies themselves.\n* The “Grade B” (pre-training) data accounts for 58.8% (24.8M) of the dataset but is defined by a permissive standard (e.g., “may have issues”). Critically, the paper lacks an ablation study to prove that this massive, known-to-be-flawed data is actually beneficial for pre-training. The provided ablations (Table 14) only test task formulation, not the impact of data quality grades (A vs. B).\n* The pipeline lacks validation for individual stages, making it vulnerable to error propagation. Specifically: (a) the segmentation accuracy of the foundational CXAS model (for 257 regions) is not reported; (b) the information extraction performance of Llama-3.1-70B is not quantified (e.g., F1 score); (c) or benchmarked against RadGraph or Chest ImaGenome by using their gold standard dataset.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "tIQOQchTBw", "reviewer": "Reviewer_wooh", "summary": "The paper proposes MIMIC-Ext-CXR-QBA (or CXR-QBA), a large VQA datset derived from MIMIC-CXR. Using an automated pipeline—LLM-based information extraction —the authors first construct scene graphs for chest X-rays and then automatically generate question–answer (QA) pairs with multi-part, structured answers, bounding boxes, and structured tags. An automatic quality assessment filters these into ~31.2M pre-training and ~7.5M fine-tuning pairs. The paper also reports expert validation steps, introduces a structured VQA formulation for CXRs, and releases a baseline model and tools.", "questions": "## LLM reliability and bias\n\n> L243-244: More specifically, we use Llama 3.1 8B (Grattafiori et al., 2024) to rate questions and answers by the following five criteria\n\n1. LLM hallucination and judgement bias are well-documented in general / medical LLM-as-judge literature; 8B judges in particular are fragile compared with larger or ensemble judges.\n\n2. The proposed pipeline is based on scene-graph information extraction on general-domain LLaMA-3.1. Why didn’t you use (or at least cross-check against) medically tuned LLMs/IE models? The authors should at least provide a head-to-head on a radiologist-labeled dataset (entity/relation P/R/F1, negation/uncertainty, ...), and justify keeping a general model if it underperforms medical LLMs.\n\n3. If the LLM extractor and LLM judge share architecture/family (Llama 3.1), what safeguards prevent model-family bias or circular validation (the judge favoring its own style)?\n\n## Dataset\n\n4. Generally, the ontology, negation/uncertainty handling, and mapping rules need rigorous specification to be reproducible and comparable to RadGraph and ImaGenome schemas. What are the macro/micro P/R/F1 of your LLM-IE compared with RadGraph baselines? Where do errors concentrate?\n\n> Figure 5, Distribution of tags (finding subcategories, regions, findings) mentioned in answers of different question types (indication, study abnormality, region abnormality, finding). \n\n5. CXR is highly long-tailed; without balancing and leakage controls, models might learn shortcuts and inflate metrics. \n\n    5.1 In some of these questions, the question types have all positive or all negative (e.g., in  region abnormality or finding). How do the authors solve the issue of imbalanced dataset? \n\n    5.2 Additionally, the authors should also disclose patient/study/report-level splits to rule out report -> QA leakage and measure sensitivity to template priors. \n\n6. Large auto-generated corpora often contain heavy redundancy that distorts training and evaluation. What fraction of QAs are near-duplicates (by text, tags, question types, and box layout)? The authors should provide more about the dedup pipeline (e.g., MinHash/SimHash + ANN, or semantic-hashing) and show how dedup affects baseline performance and diversity.\n\n>  For comparison, we thus select MAIRA-2 (Bannur et al., 2024), the only publicly available CXR report generation model that supports bounding box prediction. \n\n7. Beyond MAIRA-2, how does the structured-VQA model transfer to other independent Med-VQA/generation datasets and phrase-grounding tasks without template leakage? E.g., results on some common medical benchmarks (e.g., VQA-RAD, SLAKE, and PathVQA)?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- If substantiated, a CXR VQA dataset of 31M pre-training and 7.5M fine-tuning grade QA-pairs (w. box-localized, tag-structured), might be valuable to the community\n- The dataset construction pipeline might also be helpful.", "weaknesses": "- Reliance on LLM-information extraction and LLM-as-judge without deep radiologist ground truth\n- The issue of dataset balance, diversity, and repetition", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)