Learning Self-Critiquing Mechanisms for Region-Guided Chest X-Ray Report Generation
Authors: Sixing Yan
Ziao Wang
Kejing Yin
William K. Cheung
Ka Chun Cheung
Simon See
Venue: ICLR 2026 Poster (2026)
Keywords: radiology report generation
x-ray report generation
self-critiquing mechanism
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: Learning self-critiquing mechanisms to improve the abnormality localization for accurate report generation.

Abstract: Automatic radiology reporting assists radiologists in diagnosing abnormalities in radiology images, where grounding the automatic diagnosis with abnormality locations is important for the report interpretability. However, existing supervised-learning methods could lead to learning the superficial statistical correlations between images and reports, lacking multi-faceted reasoning to critique the relevant regions on which radiologists would focus. Recently, self-critical reasoning has been investigated in test-time scaling approaches to alleviate hallucinations of LLMs with increased time complexity. In this work, we focus on chest X-ray report generation with particular focus on clinical accuracy, where self-critical reasoning is alternatively introduced into the model architecture and their training objective, preferred by the real-time automatic reporting system. In particular, three types of self-critical reasoning are proposed to critique the hypotheses of grounded abnormalities compared to i) alternative abnormalities, ii) alternative patient's X-ray image, and iii) potential false negative abnormalities. To realize this, we propose a novel Radiology Self-Critiquing Reporting (RadSCR) framework, which constructs the abnormality proposals for each localized abnormality region and verify them by the proposed self-critiquing mechanisms accordingly. The critiqued results of the abnormality proposals are then integrated to generate the completed report with interpretable diagnostic process. Our experiments show the state-of-the-art performance achieved by RadSCR in the grounded report generation and diagnosis critiquing, demonstrating its effectiveness in generating the clinically accurate report.

Published as a conference paper at ICLR 2026
LEARNING SELF-CRITIQUING MECHANISMS FOR REGION-GUIDED CHEST X-RAY REPORT GENERA- TION
Sixing Yan!.;; Ziao Wang", Kejing Yin?! William Cheung,, Ka Chun Cheung & Simon See
1 Postdoctoral Research Station, Guangxi Rural Commercial United Bank Co., Ltd
2 Department of Computer Science, Hong Kong Baptist University
3 NVIDIA AI Technology Center, NVIDIA Corporation
ABSTRACT
Clinically accurate and interpretable automatic radiology reporting requires re- liably grounding the identified abnormalitics with the corresponding regions lo- cated in the radiology image. In this paper, we propose to introduce self-critiquing mechanisms into the automatic report generation process to ensure the identified abnormalities can reliably grounded before they are reported. Instead of adopting LLM-based reasoning to implement the self-critiquing mechanisms which will in- cur high inference cost in test time, we propose novel Radiology Self-Critiquing Reporting (RadSCR) model framework which allows multi- fuuli--fccted mechanisms be learned end-to-end to identify and verify some hypothesized abnormality regions by comparing with i) alternative abnormalities, ii) alternative patients' X-ray images, and iii) potential false negatives. The self-critiqued abnormality proposals are then integrated using a retrieval-based approach to generate the final report. Our experimental results show that RadSCR can outperform the state-of- the-art art report generation methods in terms of clinical accuracy by a large margin, with improved reliability of abnormality localization.
INTRODUCTION
Automated radiology image reporting aims to reduce radiologists' workloads on report preparation. Recent development of deep learning models for generating X-ray reports has shown continuous improvement on clinical accuracy Chen al. (2020) Yan et al. (2023); Wang et al. (2024c). Yet, how to reliably grounding generated report with the regions of the abnormalities identified in the images remains open, which is important as this what radiologists carry out in practice. In this paper, we propose to incorporate self-critiquing mechanisms into deep learning models for generating X-ray reports so that the reliable grounding of the abnormality findings can be established.
Grounding radiology images with abnormality findings using deep learning models is non-trivial as large-scale annotations of abnormality regions are still lacking. Some recent works explored anatomy-awarenee by making reference to detected anatomical parts (e.g.. lung, heart, etc.) in the image for grounding the findings, resulting in higher accuracy and better interpretability (Tanida et al. 2023 Li et al. 2024 Dalla Serra al., 2023) Yan et al., 2024). In practice, more fine-grained abnormality regions are generally preferred for grounding. Also, carefully examining potential ab- normality regions is often unavoidable if reliable radiology report is to be prepared.
Self-critiquing is commonly adopted by medical professionals to reduce the chance of making di- agnosis mistakes. In the context of report preparation, it refers to the process where radiologists identify and validate the potential abnormality regions on the X-ray images before findings are con- cluded. Existing automatic radiology reporting models are mostly trained based on statistical cor- relations between regions and paired sentences (Fallahpour et al. 2025 Gai et al. 2024: Fan et al.)
"This work was done when Sixing Yan was a Postdoctoral Fellow the Department Computer Science,
Hong Kong Baptist University. Correspondence author: Kejing Yin hattyyi@@ooom.hhh...de.o

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Published as a conference paper at ICLR 2026
2023).
resulting in unavoidable hallucinations. The idea of self-critiquing is still under-explored except for a few works on visual question answering (Cheng et al., 2025 Wu & Mooney 2019).
We argue that reliable radiology report generation requires "multi-faceted" self-critiquing mecha- nisms for establishing reliable grounding of potential abnormalities. While large language model- based paradigms like thain-of-thought have recently been explored to introduce test-time reasoning to alleviate hallucination (Wu & Mooney 2019. Cheng et al. 2024] Cocchi et al.] (025), we con- sider alternatives as LLM-based reasoning typically generates long chain of "thinking" tokens during inference and incurs high test-time cost (Huang et al. 2025 Geiping et al. 2025). Also,
deploying LLMs for applications with low-resource environment is non-trivial. Our idea in- corporate multi-faceted self-critiquin mechanisms into the model architecture to be learned during the training. without requiring test-time scaling.
To this end, we propose novel Radiology which adopts region-guided chest X-ray report generation paradigm with self-critiuingg mecha- nisms incorporated to mimic the self-critiquing thinking process of radiologists for enhancing the report's reliability. RadSCR first identifies an initial set of fine-grained visual proposals, each rep- resented by triplet of abnormality region, abnormality label and the corresponding visual features. Self-critiqunng is then realized by cross-checking the hypothesized visual proposals to see their visual features distinct and relevant enough for the associated abnormalities. In particular, it explores alternative abnormalities and alternative patients' X-ray images, and then further takes a holistic view of the image to double-check the possibility of missing abnormalities. The visual pro- posals "discounted" by the possible alternatives are considered together for retrieving appropriate sentences of abnormality findings from a report repository to be integrated by an LLM decoder to generate the final report. We carried out comprehensive experiments to evaluate the effectiveness of RadSCR using a variety of datasets including MIMIC CXR, ReXGradient, and IU X-ray. Our experimental results demonstrate that RadSCR outperforms all the state-of thr report generation baselines by a large margin, with improved localization of abnormality regions for grounding the findings. The main contributions of the proposed RadSCR include:
Reporting (RadSCR) model framework
providing an automatic radiology reporting methodology guided by abnormality regions for more fine-grained grounding of abnormality findings;
introducing self-critiquing mechanisms into deep model architecture for more reliable grounding without the need to introduce LLM-based reasoning in test time;
demonstrating via comprehensive empirical evaluation the effectiveness of introducing self-critiquing mechanisms to achieve clinically accurate radiology X-ray reporting.
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RELATED WORKS
Grounded Radiology Report Generation The grounding of the generated findings of the report within the relevant regions radiology images is important for medical image understanding and diagnosis (Bannur et al. 2024). Various well-designed attention mechanism modules have been proposed locate abnormality region of interests (ROIs) for X-ray report generation. WWang et al. (2024a) proposed to use class activation mapping (CAM) (Zhou et al., 2016) to guide the visual attention module to identify regions of abnormalities, where weighttd maps are obtained from a multi-abnormality classifier head topped at the visual encoder. Alternatively, the anatomy- awareness approach tries to locate the anatomical parts relevant to the findings generated for ground- ing. RGRG (Tanida et al.) 2023) and ORGAN (Hou et al., (023b) use a shared visual extractor to de- tect the regions of the anatomical parts and then generate the report accordingly. BoxMed-RL (Jing et al. 2025). MedPromptX (Shaaban et al.) 2024) and MAIRA-2 (Bannur et al. (2024) learn to anno- tate the anatomical regions and detect the possible pathology labels, followed by report generation. However, localizing anatomical parts is not precise enough for the grounding purpose.
Radiology Reasoning Visual Question Answering Radiology reporting typically involves a multi-step diagnostic process to identify and locate abnormalities revealed in the image. Recent advances in LLM--baed reasoning approaches use chain-of-thouge of-thought (CoT) to represent the process for report generation. For instance, MedCoT (Liu et al.) 2024) incorporates several LLMs hi- erarchical experts by CoT, where each expert's 's output is further verified by a subsequent expert. MedRAX (Fallahpour et al. 2025) decompose radiology image diagnosis into sequence of tasks
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and use multiple pre-trained models agents solve each task. ChestX-Reasoner (Fan al. 2025 further decomposes each diagnostic finding of the report into a step-by-step CoT where each CoT contains textual description, anatomical region, and sspert-labeles clinical notes. Instead of using CoT, RECAP (Hou et al. 2023a) and ORGAN (Hou et al. 2023b) implement diagnostic reason- ing by finding proper graph walk in a pre-constructed knowledge graph of clinical findings. Our proposed RadSCR does not employ reasoning token generation in test time as CoT and achieves self-critiquing by incorporating that directly into the model architecture. In the literature, there exist some recent works which also take the "what-if" approach as RadSCR for more reliable radiology report generation. PGFC (Mahmood et al. 2025) uses a fact-checking model to determine whether pair of a clinical finding and an anatomical region match with each other not. CoFE (Li et al.) 2025) creates counterfactual explanations by replacing patches on an X-ray image until the diagnosis changes for contrastive learning, where localization and grounding are not considered. In contrast, our proposed RadSCR critiques the predicted abnormality location by considering alternative abnor- mality and alternative image, and false negative checking; and use these critiquing to enhance the sentence retrieval reliability for report generation without generating counterfactual explanations.
Weakly Supervised Abnormality Localization Lacking large-scale annotation of abnormality gions in X-ray images makes supervised learning of abnormality localization difficult. Weakly- supervised learning approaches for abnormality localization have been investigated. Attention mechanisms are learned to attend regions of abnormalities, where abnormalities are then classi- fied based on the visual features of these regions (Li et al. 2018). Anatomical areas can also be used to restrict potential regions for subsequent localization Yu et al. (022). With similar idea, some coarse coarse-grained abnormal regions can first be grounded before localizing the regions of specific abnormalities (Ouyang et al. 2020 Wang et al.] (2024b). In general, how to precisely localize the regions (not to over-cover or over-look) for grounding the report generation remains open.
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Figure 1: overall model architecture of the proposed RadSCR for radiology report generation.
METHODS
Given radiology image T, our proposed Radiology Self-Critica Reporting (RadSCR) framework generates radiology report with findings abnormalities grounded with abnormality regions identified in I. First, set of visual proposals of abnormalities is hypothesized. To obtain the visual proposals, an X-ray image I encoded as the visual features V is first fed to classifier to predict the presence of a set of N abnormalities. The potential region of the m-th predicted positive abnormality, represented as bounding box B,,, can be located using Grad-CAM (Selvaraju et al, 2017). For each abnormality, concept embedding E.,, is be learned. A region-based visual
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proposal of the m-th abnormality is thus denoted triple (V, Bm: Em), and represented as: Pm PropEncoder(V. Bm: Em).
Then, the hypothesized visual proposals are critiqued for their distincttivness and relevancy using multi-faccted self-critiquing mechanisms. Three mechanisms introduced to critique the visual proposals (Pm) by proposing i) alternative abnormalities, ii) alternative patient X-ray images, and iii) potentially missing abnormalities, represented respectively {C%Abn),, J. ( {CHlm)), and ('(Ne)) Each hypothesized visual proposal Pm. is considered together their corresponding alternative visual proposals ((Abm) and C(Imu)) to retrieve relevant sentences from the report repository, denoted as Qm. ((Neg) is used to retrieve additional sentences of potential false negatives which could be missed in the localization step, denoted as U. They are then fed to an L.LM decoder with pre- defined prompt for sentence aggregation and final report generation. The process is denoted as:
{[Qm), U} = Retrieve ({(ee C(Abb), Cm (1 Fig. 1 provides an overview of the RadSCR framework, with details to be presented.
R = (ennerate((Qm)))))
C(Im)))) C(NeR)). (2)
3.1 CONSTRUCTING RADIOLOGY VISUAL PROPOSALS
We represent the visual features E RHWxD of an X-ray image I as a patch map H x W with D-dimensional features encoded by visual encoder, denoted as V = VisEncoder(T).
Initial Abnormality Prediction
The presence of the m-th abnormality is predicted by feeding the average visual features of V over the dimension of HW to fully connected network FCN: R R--,V where the predicted probability is computed as:
Pm Sigmoid (Avgoooo
The M abnormalities with Pm > 0.5 form the set of potential (positive) abnormalities in
Abnormality Region Localization: The relevant regions on the image associated with each pre- dicted positive abnormality are then be localized. As conventional object detectors do not gen- eralize well to out-of-distribution (OOD) abnormalities, we leverage the class activation mapping (CAM) to compute the pixel-level saliency on the image I for each predicted positive abnormal- (ALM(VisEnnodee) 7, m) (as shown
ity Fu et al. (2020), with the saliency map denoted as Wi, in Fig. Then, a set of corresponding bounding boxes, denoted as BBozes = [bm,. 1. 2 is estimated based on W.. using the objectness estimation algorithm proposed by Cheng et al. (2014),
where bm, a patch-level mask sharing the same size of I with the patches set as I to indicate the presence of the m-th abnormality, or otherwise. The localized region of the mhh abnormality Bm, E TR HW is then formed by the union of the bounding boxes in BBores, given as:
B Map (MaxPool (o0mm, Dm,,,,..)
We conceptualize the A abnormalities and the common chest X-ray background using (W+1) global concept representations E E (V++) (to be learned Jm=1 corresponds to the concept representations of the N abnormalities, and Ex+1 I is the padding representation of the background.
Region-based Visual Proposal Encoding:
as detailed in Section 3.3 Em E IR 12xD)Ar'
The region- based visual proposal for the m-th positive abnormality (V, Em) is represented as: P... PropEncoderr(*. Bm, Em). We implement the proposal encoder by first obtaining the spatial- -aware abnormality representation Fm, based on abnormality region mask Bn with the con- cept representation Em, and then concatenating it with the visual features V at the patch level to obtain an abnormality proposal representation P... E RHWXD given as
P.. FFN(V a F..); Fm Embedding Bm, Em);
(5)
where Embedding() replaces the slots of abnormality localization in Bm, with E,, and the remain- ing slots with padding embedding Ex+1, and FFN(-) is two-layer berd-forward network.
3.2 LEARNING CELF-CRITIQUING MECHANISMS
To ensure the reliability of the visual proposals, we mimic the thinking process of radiologists to re- examine the proposals' correctness by considering i) alternative abnormalities, ii) alternative patient
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X-ray image, and iii) potential false negative abnormalities. With reference to each visual proposal (V, B,, Em), we modify different components to form additional proposals for the critiquing.
Critiquing Based Alternative Abnormalities For chest X-ray images, some abnormalities with similar appearance are hard to differentiate. To self-critique if a positive abnormality is distinct enough as compared to other abnormalities, we compute the visual proposal of alternative abnor- mality (V, Bm, F,,) where E',, can be the concept representation of an abnormality selected from the predicted negative abnormalities or those predicted positive but in some areas not covered by Bm. The corresponding representation is given as:
C(AAm) = PropEncoderr(, Bm, F.y.)
(6)
Critiquing Based on Alternative X-ray Images To check if visual proposal is specific enough and relevant for diagnosing the abnormality among patients with different abnormalities, form another type of alternative proposal for critiquing by replacing the visual features of the hypothe- sized visual proposal (V) with those of randomly selected image from other patients (V"). The representation of the critique is obtained by:
(Iimg = PropEncoderr(V' Bun F.n). m
Critiquing by Considering Potential False Negatives: As all visual proposals are hypothesized using fine-grained localization, abnormalities characterized by more holistic features could be "over- looked". To double-check for such false negatives, we leverage the global concept representations of the predicted negative abnormalities to make an additional complementary visual proposal. Localiz- ing the predicted negatives is non-trivial as reliable bounded boxes are absent. Instead, we compute an overall complementary visual proposal by taking average pooling of the concept representa- tion of all the predicted negatives, given as Eg AvgPool [Em Pm < 0.5, m E [1,W]]). We then associate it with region by aggregating the bounding boxes of the major anatomical parts in the Chest region based on the automatic detection tool (Seibold et al. 2023) if not annotated, denoted as By. The visual proposal is thus denoted as (V, Eo). and represented as:
((Neg) = PropEncoder Bo, Eo).
(8)
3.3 RETRIEVAL-BASER REPORT GENERATION WITH SELF-CRITIQUING
With an X-ray image represented as (f(P m. C (Ab)) (((mm))) }}
(Neg)) we adopt the sarrevallbasse
approach for report generation (Endo et al.] 202T; Yang et al., 202T Ranjit et al.) [023) Yan al.,
2024). Sentences of relevant findings matched with the image are first retrieved from repository of radiology reports and then combined using an L.M to generate the final report.
We represent sentence annotated with the m-th abnormality in a report as ((m). For each report, we concatenate all sentences of the same abnor- mality into one to ease the subsequent retrieval. To allow robust retrieval of 8(m) based on the visual proposal Pm. we argue that it is important for not only 5(m) aligning well with Pm., but also its higher-level clinical concepts (called "prototypes" in the following sections). For each abnormality, we assume IC prototypes, represented as O == {oR | We learn the sentence representation 8(m) and the prototype representation Opt(s(n)) that they are close to P... over all the sentences in the repository, where pt(s) gives the index of s's associated prototype.
Representations of Sentence and Prototype:
To compute (m)) we first apply pre-trained ClinicalBERT Yan & Pei 2022) to obtain Stert E R I where 1 is the sentence length, and then cross-attention representation E' to pick up the associated semantics. Then, self-attention Attna() with average pooling is used to obtain the sentence representation 5(m) E <RIXD;
Attnx() between Sbert with the concept
8 (m) AvgPool AAgtool(Attt,,T], m: Ty, Th,)) where
T = AAttn Fmm Fmm)
To derive the prototypes O, we first apply IC-means to the TF-IDF representation of the sentences in the report repository. The sentence-cluster association is then fixed. The representation of the k-th prototype is initialized by applying average pooling to the representations of the associated sentences, denoted Og AvgPool((s (s)) k)), and will be optimized during training.
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Retrieving Relevant Sentences with Self-Critiqunng and Report Generation: Given the visual proposal Pm. relevant sentences are retrieved among those annotated with the mhth abnormality to support the report generation. To support more robust retrieval of relevant sentences, compute the similarity score between the visual proposal Pm and a sentence 8(m) by considering also the sentence's prototype, given as:
o ( ((m)) Pm @ ((m) + am O Opt(s(m))>
(10)
where is the dot product and 0i the importance weight of the prototype-based similarity. For enhancing reliability, we incorporate the alternative visual proposals C(Abu) and C(lmg) to suppress Pn. and discount the similarity score as:
(Pmm: (m)) = o(Pms (()) - a2 (o((AA)))) ,(m) + o (CC ,(m)))
(11)
where ag is the importance weight of the alternative proposals. The top-M sentences retrieved based on the discounted similarity score ō (Ppm: 8(m)) form the candidate set of sentences {Qm J JM m==1.
For the complementary visual proposal ((Nee), the of prototypes is not needed the predicted negatives aggregated together in our formulation. The similarity score between (Nee) and the sentences corresponding to En (denoted as 8(0)) can be computed by oCCNee)) *(o)) C(Neg) (
$(0). The top-(N-M) sentences based o (C(Ne))) s(()) form the complementary set of candidate sentences denoted as { W-M
To generate the final report R, LLM is adopted to integrate the retrieved results using prompt:
R = LLM (Prom(Porpp(()a)) I WU (M)))
(12)
In our experiment, the Prompt is designed so that all sentences in Q are expected to be used for report generation, while those in U are only used if they do not contradict Q (as shown in Fig. 3).
3.4 Loss FUNCTION FOR MODEL LEARNING
The RadSCR model M designed with the following learnable components: the visual encoder VisEncoder(), the initial abnormality predictor FCN(), the abnormality concept representations {Em}. the visual proposal encoder PropEncoder(). the sentence attention mechanisms Attn, () and (t, and the prototype representations O. For model learning, we define objective function with a set of loss terms to achieve reliable retrieval.
Loss for Visual-Language Alignment: For each image-report pair (indexed by i) in the training batch B, each underlying visual proposal Pi, should be close to the positive samples which are the ground-truth sentence ((m) and the prototype of po((t..))" but far from the negative samples containing
sentences of: a) other abnormalities (8{-m)) := ( (ie()..M))m b) same abnormality but in dif- ferent reports {s7m)) } == (1).))=111..111 and c) same abnormality but with different prototypes (o -pp(((()))) = (01 The loss term is thus defined as:
((prop) = Lo(Pi, m. (()): {s(-m) }) + (P. (s + Lo in Opt(s))m)) to ppt(st )))
H).
(13)
where Lp,p, {pos}, (neg}) refers to the symmetric contrastive loss to force p close to the positive
samples {pos} and far from the negative samples {neg} (Radford et al. 2021). By optimizing
Eq. (13). the representations of the visual proposals, sentences and prototypes on one hand will be aligned. Due to end-to-end learning. the abnormality localization is also learned in a weakly- supervised manner. Note that we drop the index in the following sections for the clarity.
Loss for Self-critiqinng: To learn the self-critiquing mechanisms, we make use of triplet loss to push 8m)) close to Pm but apart from alternative proposals C ((Aba) and (lmg)) to improve retrieval reliability. The loss term is defined as:
M Σ
(LT()(m)) Pm, On C(Abn)) + Lr(s(m)) Pm, (.,,C(lmg)) C(lmng));
(14)
2M m=l

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where Cr,a, pos, neg) is triplet loss with a being the anchor, pos the positive sample, and neg the negative sample.
For false negative self-critiquing, we define an additional contrastive loss to guide C(Neg) to be close to the sentences with abnormalities present but not hypothesized (positive mentions) (&)) but far from those with abnormalities absent from being mentioned (negative mentions) {&))
[(Neg) Lo(C(Ne)) (8 (
(15)
We train the proposed RadSCR model by optimizing: ((Pro)) + 31 (( + (2L(Nog) with the importance weights 31 and B2.
EXPERIMENT
Data We test the proposed RadSCR on three publicly available X-ray image-report datasets MIMIC
CXR (Johnson al., 2019a b). ReXGradient (Zhang et al., 2025) and IU XRay (Demner-Fushman
ct al. 2016) for report generation, report retrieval and abnormality detection. We also use VinDR-
CXR (guuen et al.
2022 to test the performance of abnormality localization on X-ray images.
Baselines For performance comparison, we evaluate a set of state-of-the-arr approaches, includ-
ing i) VLM-based approaches: Trans former (Vaswani et al.
(017)), R2Gen (Chen et al.
2020). R2GenCMN Chen et al. (202T) RGRG (Tanida et al. (203))
11
LLM-based: Qwen3-Vi
(3B) Yang al. 2025), MedGamma (4B) (Sellergren
al. 2025). LLaVA-Med (7B) Li
et al. 2023).
(7B) (Zambrano Chaves et al. 2025), 3. COMT
(7B) (Jiang et al. 2025).
and iii) retrieval-based approaches: BiomedCLIP (Zhang et al. 2023). MedCLIP Wang et al.
(2022). ) BioViL (Boecking et al. [2022), X- -EEM (Jeong et al. 2023) and CXR-RePaiR [ndo
al. 2021). Related implementation details are reported in the Appendix A.1.
Model Setting We train RadSCR on MIMIC CXR (training set) and evaluate it by the test sets of MIMIC CXR, ReXGradient and IU XRay. We use the Swin Transformer (Liu et al. (021) as visual extractor and Phi (4B) (Ren et al. 2025) as the LLM decoder with its parameters frozen. We consider N = 37 abnormalities annotated by Chest ImaGenome Wu et al. (202T). The prototype number is set to K. = 5 for each abnormality, where sentences with positive mentions are clustered into 4 groups and those with negative mentions form the remaining one.
4.1 PERFORMANCE EVALUATION ON REPORT GENERATION
Evaluation Metries:
2020) of 14 We evaluate the generated reports by i) CheXbert (Smit et al.
observation accuracy, ii) Clinical Efficacy (CE) (Chen et al. 2020) extended to 37 abnormalities (CE-Abn) and the normality of 25 anatomical parts (CE-Organ), fii) RadGraph- F1 (Jain et al, 2021 which also considers relationship correctness among observations, iv) RadNLI (Miura al. 2021 which measures inference correctness of contradiction, entailment or neutral between gener- ated reports and ground-truth, and v) BLEU (Papineni et al., (002), METEOR (Banerjee & Lavie, 2005) and ROUGE-L I (Lin) (004) for measuring n-gram accuracy. The ground-truth annotations are used targets in computing the metrics.
Experimental Results and Discussion: We conduct extensive experiments for performance eval- uation based MIMIC CXR, ReXGradient and IU Xray datasets. Table [ shows the results on MIMIC CXR. Among baselines, LLaVA-Rad shows effective performance in diagnosing common chest abnormalities evaluated by CheXbert, and the region-based RGRG shows high accuracy of reporting clinical observations relationship (occurrence and anatomical location) evaluated by RadGraph-F1. RadSCR outperforms other baselines both clinical accuracy metrics CheXbert rt. and RadGraph-F1. Meanwhile, RadSCR gives the best performance in the detection of anatom- ical abnormalities. as indicated by CE-Abo and CE (5--rraan scores, covering a wide range of chest abnormalities and their anatomical locations. In addition, RadSCR gives the highest RadNLI scores, indicating fewer diagnostic statements contradictory to the ground truth in the generated re- ports. These results suggest that RadSCR effectively improve clinical accuracy in terms of both abnormality detection and diagnostic coherence to the ground-truth for report generation. Similar results obtained for ReXGradient and IU Xray datasets (see Appendix A.1 for more details). We

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CheXbert
CE
RadGraph-Fi
RadNLI
Model
Acc.
F-1
Abn.
Organ
Pr.
Re.
F-1
Transformer
0.201
0.304
0.208
0.269
0.191
0.130
0.161
0.217
0.135
VI.M- based
R2Gen
0.203
0.303
0.207
0.476
0.205
0.243
0.168
0.187
0.128
R2Gen-CMN
0.157
0.402
0.258
0.416
0.201
0.137
0.144
0.199
0.109
RGRG
0.383
0.489
0.251
0.669
0.321
0.248
0.379
0.326
0.317
Qwen3-VI.
0.184
0,195
0.065
0.289
0.081
0.046
0.253
0.160
0.112
MedGamma
0.419
0.413
0.219
0,407
0.141
0,086
0.469
0.143
0.150
LMM-
based
LLaVA-Med
0.397
0.135
0.041
0.555
0.202
0.139
0.332
0.335
0.312
LLaVA-Rad
0.487
0.512
0.399
0.661
0.285
0.220
0.314
0.322
0.286
CoMT
0.406
0.250
0.151
0.485
0.218
0.151
0.331
0.290
0.274
BiomedCLIP
0,309
0.221
0.184
0.675
0.235
0.175
0.335
0.314
0.305
BioViL
0,403
0.367
0.325
0.595
0.232
0.173
0.300
0.302
0.274
Retrieval -based
MedCLIP
0.032
0.297
0.106
0.153
0.112
0.071
0.175
0.25
0.161
CXR-RePaiR
0.385
0.423
0.380
0.630
0.251
0.191
0.293
0.292
0.264
X-REM
0.382
0.402
0.382
0.615
0.243
0.186
0.303
0.310
0.280
RadSCR
0.574
0.610
0.572
0.744
0.422
0.367
0,440
0.433
0.408
Table I:
Performance comparison on report generation based on MIMIC CXR data. "P." and "C.":
Partial and Complete correctness of observation relationship; "Pr." and "Re,": Precision and Recall.
CheXbert
CE
RadGrap
RadNLI
Model
Acc.
F-1
Abn.
Organ
P.
C.
Pr.
Re.
F-1
RadFM
0.566
0.635
0.545
0.652
0.399
0.367
0.432
0.401
0.395
MAIRA-2
0,581
0,621
0.565
0.701
0.444
0.379
0.445
0.422
0,410
RadSCR
0.574
0.610
0.572
0.744
0.422
0.367
0.440
0.433
0.408
Table 2: Performance comparison on MIMIC CXR against some recent models with larger decoders.
further compared RadSCR with some more recent models with larger decoders. including MAIRA- 2 (7B) (Bannur et al. 2024) and RadFM (Wu et al. 2025 shown in Table Although they have significantly larger model sizes, our proposed RasSCR still achieved comparable results.
Ablation Study: To better understand the impact of different components in RadSCR, abla- tion study is conducted by removing (i) self-critiquing mechanisms in both training and testing, (ii) self-critiquing mechanisms in testing only, (iii) L.M decoder, and (iv) abnormality prototypes. The results are shown in Table Referring to (i) and (ii), removing the self-critiquing mechanisms leads to obvious performance degradation. Mcanwhile, the self-critiquing mechanism inference can ensure generated reports of better quality (see (ii)). In addition, by eliminating (Nee), the scores of both CheXbert (F-1) and CE-Abn drop, indicating the lower accuracy of abnormality detection. shows that the global features used by C(Neg) could help RadSCR identify certain missed abnor- malities. Furthermore, results in (iii) indicate drops in the RadNLI score when LLM is removed, indicating its role in ensuring the content coherence of the retrieved sentences and the generated report. Also, results in (iv) show that removing the prototypes leads to drops in CE-Organ and RadGraph-PP scores which also consider the accuracy of the associated anatomical parts. The use of prototypes allows sentences of the same abnormalities with context variations (e.g., regions of observation) to be better organized for more fine-grained retrieval.
Effect of Sampling More Alternative Proposals: We can extend the self-critiquing mechanisms by
sampling more alternative proposals (Abb)) and ((Img) We first tested the number alternatives randomly sampled Np from zero to three for both C(Aba) and ((mmg), with the results shown in Fig. 2 N₂ O indicates that alternative is considered. For cases with Np > 1, the averaged effect of multiple sampled alternatives are computed for discounting the hypothesized proposal. By jointly increasing the number of (AAb)) and (lugg) the best performance obtained at N, 2, where 2 x .M alternative abnormalities and 2 x .M alternative X-ray images are considered. While the result implies that considering multiple alternatives can improve the effectiveness of the self- critiquing mechanisms, the optimal ways of sampling the alternatives remain open. We tested also different schemes of controlling randomness based on the patients' metadata and achieved further performance improvement (see Appendix A.1 for more details).
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RadSCR w/o.
CheXbert(--1)
CE-Abn
CE--Organ
(adGrrahh
RadNLI(1-1)
0.610
0.572
0.744
0.367
0.408
((mmg)
0.581
0.542
0.691
0.371
0.388
ClAbb)
0,560
0,523
0.688
0,300
0.369
((Neg)
0.602
0.556
0.709
0.345
0.367
((+)
0.561
0.535
0.689
0.289
0,359
(
0.605
0.450
0.669
0.311
0.32.5..
(ii)
((Aba)
0.577
0.465
0.64814
0.253
0.335
C(NNe)
0.49144
0.545
0.668
0.354
0.398
C(+)
0,545,
0.3794
0.653
0.231
0.343
(iii)
LLM
0.611
0.354
0.724
0.351
0.326.
(iv)
(Ox JR-1
0.591
0.37744
0.751
0.2101J
0.357
Table 3: Results of ablation study by MIMIC CXR. C++) refers {C(Imm) C(Abb)) C(Neg)) "I
and "I" indicate the scores with the largest and second largest drops, respectively.
In addition, other than random sampling. we further investigated the effectiveness of other strategic schemes of sampling alternatives, including sampling those most similar to the visual proposal (hard samples), sampling those least similar (easy samples), well sampling mixture of them. As shown in Table some strategic sampling schemes (e.g., Random-+hard+easy can further boost the performance for some metrics. In general, how better sample the alternatives achieve more effective self-critiquing is an interesting direction of extending the RadSCR s framework.
0.750
0.380
0.610
0.740
0.400
0.600
0.720
0.340
0.320
0.590
0.360
0.600
0.580
0.660
0.280
-0- Casn
-2_ Cira
0.570
0.640
0.260
0.320
+ CheaCh
N,
N
N,
(a) CheXbert (F-1)
(b) CE-Abn.
CE-Organ
(d) RadGraph (Complete)
RadNLlI
Figure 2: Performance of RadSCR with different numbers alternative proposals sampled.
CheXbert
CE
RadGraph-Fl P.
RadNLI
Sampling
Acc.
F-1
Abn.
Organ
C.
Pr.
Re.
F-1
One alternative abnormality sampling
Random
0.574
0.610
0.572
0.744
0.422
0.367
0.440
0.433
0.408
Only hard sample
0.561
0.577
0.532
0.720
0.432
0.362
0.444
0.451
0.413
Only easy sample
0.568
0.593
0.566
0.751
0.426
0.361
0.432
0.412
0.399
Three alternative ahnormalities sampling
Random
0.570
0.609
0.577
0.731
0.441
0.377
0.448
0.455
0.419
Random-hardd+essy
0.575
0.606
0.583
0.739
0.446
0.381
0.442
0.462
0.421
Table 4: Performance of different alternatives sampling schemes for critiquing on MIMIC CXR.
4.2 PERFORMANCE EVALUATION ON RETRIEVAL RESULTS
To further evaluate the ranking quality of the sentence retrieval results, we make use of Accuracy-K and some preference ordering metrics. The former gives the percentage of target sentences found in the top-K results. For the latter, consider the top-50 results and measure the percentage of sentences with correct positive diagnoses ranking higher than the following three types of less preferred sentences: i) Incomplete: sentences with correct positive abnormalities but incomplete, fi) Partially Correct: sentences with correct and incorrect positive abnormalities, and iii) Incorrect: sentences without correct positive abnormalities. We denote the three preference ordering (PO)

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Published as a conference paper at ICLR 2026
metrics PO-1, PO-2 and PO-3. As shown in Table RadSCR performs consistently better than the baselines for highly-ranker sentences as indicated by Acce5 and Acce 10. It also achieves the best preference ordering scores, indicating its effectiveness in preserving overall sentence ranking.
IR Accuracy
Preference Order
Model
Acc@5
Acc@ 10
PO-1 (Incomplete)
PO-2 (Partially Correct)
PO-3 (Incorrect)
CXR-RePaiR
0.106
0.106
0.010
0.015
0.026
BiomedCLIP
0.266
0.288
0.478
0,514
0,467
McdCLIP
0,010
0.042
0,480
0,534
0,432
BioViL
0.171
0.171
0.523
0.528
0.523
X-REM
0.243
0.302
0.023
0.025
0.031
RadSCR
0.277
0.347
0.644
0.659
0.606
Table Comparing the quality of sentence retrieval based on MIMIC CXR dataset.
4.3 PERFORMANCE EVALCATION ON ABNORMALITY PREDICTION AND LOCALIZATION
To illustrate the importance introducing region-awareness for abnormality prediction, we cre- ate a baseline which uses again Swin Transformer (Swin) visual extractor, followed by linear projection layers (MLP) instead of the fine-tuned RadSCR for abnormality prediction. As shown in Table 6 Swin-RadSCR outperforms Swin+MLP for all accuracy metries, indicating that the proposed RadSCR with the region-awarenee awareness introduced can effectively improve the discriminative properties of the visual feature for abnormality prediction.
Model
F-1f
FPR
PR-AUCT
ROC-AUCT
Swin+MLP 0.160
0.440
0.562
0.756
Sain+RadSCR
0.208
0.425
0.703
0.900
Table 6: Evaluation abnormality prediction. FPR refers false positive rate.
We also compare RadSCR's performance abnormality localization with two existing weakly- supervised localization methods, including one based on the patch-based approach TDIL (Li et al.) 2018) and another one based on the attention-based approach HAM (Ouyang et al. 2020). The eval- uation is based on VinDR dataset, and the mean average precision score is adopted for the metrics. As shown in Table 7 with the threshold of the Intersection of Union (loU) set as 0.1/0.3/0.6 Swi n+ RadSCR can better localize the ground-truth abnormality regions by large margin com- pared the baselines (see Appendix for more details).
Model TDIL
IoU(0.1)
IoU(0.3)
IoU().5)
0.125
0.095
0.077
HAM
0.134
0.102
0.081
Swin+MLP
0.210
0.054
0.012
Swin+RadSCR
0.308
0.199
0.101
Table 7: Evaluation of abnormality localization VinDR dataset with annotations of abnormality regions. The available model weights of TDIL and HAAL used in this experiment are trained on ChestXray8 (Wang et al. 2017), and Swin+MLLP/RadSCR are trained MIMIC CXR.
CONCLUSION
We propose novel Radiology Self-Critiquing Reporting model framework called RadSCR which learn multi-faceted mechanisms to self-reflect and verify the potential abnormality regions by con- structing visual proposals of hypothesized abnormalities presented. The self-critiqued proposals are then integrated by retrieval-bar approach to generate reliable radiology reports, outperforming the SOTA report generation methods in terms of clinical accuracy and improved reliability of the located abnormality regions. Limitation The possibly false negative abnormalities critiqued in the whole chest region on the X-ray images. Thus, the critique results on these critique results might not indicate any specific abnormality region to be localized.
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ETHICS STATEMENT
The authors confirm that there are no i) human subjects or practices to data set releases, ii) potentially harmful insights, methodooogiees applications, iii) potential conflicts of interest or sponsorship, iv) disriiinatiov/iiakkinnsss concerns, v) privacy or security issues, or vi) legal compliance. The research integrity issues (e.g., IRB, documentation, research ethics) not applicable for this work.
7 REPRODUCIBILITY STATEMENT
To ensure the reproducibility of this work, the authors prepare the implementation details in the Appendix section, including Data: Three datatsets used in the experiments publicly accessible where the download links are provided in Appendix [.I]] The data pre-processing is referred to the baselines (Chen al. 2020 2021b: ii) Model Implementation: The backbone modules of the proposed model are referred to the implementation provided by hugging face co where the links of model structures and the pre-trained weights are provided Appendix [A.2] iii) Baselines: The implementation of the baselines are all referred to their official source codes and papers, where the links of pre-trained parameters of their model weights are provided in Appendix [A.5] The results may have minor variations due to the different machines deployed; and iv) Evaluation Metrics: The implementation of the evaluation metrics are presented Appendix A.4 where the evaluation details and the links of the open-source codes provided.
8 ACKNOWLEDGMENT
This work partially supported by the National Natural Science Foundation of China (62302413), the Health and Medical Research Fund (23220312), and the General Research Fund RGC/HKBU1220262 from the Research Grant Council.
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Joy T Wu, Nkechinyere N Agu, Ismini Lourentzou, Arjun Sharma, Joseph A Paguio, Jasper Yao, Edward C Dee, William Mitchell, Satyananda Kashyap, Andrea Giovannini, al. Chest imagenome dataset (version 1.0. 0). PhysioNet, 5:18, 2021.
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Juan Manuel Zambrano Chaves, Shih-Chemn Huang, Yanbo Xu, Hanwen Xu, Naoto Usuyama, Sheng Zhang, Fei Wang, Yujia Xie, Mahmoud Khademi, Ziyi Yang. et al. A clinically acces- sible small multimodal radiology model and evaluation metrie for chest x-ray findings. Nature Communications, 1601))3108, 2025.
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A APPENDIX
A.1 EXPERIMENT RESULTS ON MORE DATASETS
We present the experimental results on MIMIC CXR dataset We also tested our proposed ap- proaches with the baselines using the IU Xray and ReXGradient datasets (as shown in Table 8 As observed, our proposed RadSCR achieved SOTA performance in most clinical accuracy metrics in both datasets, demonstrating the effectiveness of using RadSCR in clinical application to generate accurate radiology reports.
We also evaluate the proposed approach and the baselines by natural language generation (NLG) metrics (BLUE, METEOR and ROUGE). The results shown in Table As observed, the proposed RadSCR achieves SOTA performances in MIMIC CXR and ReX ReXGradient datasets. We noted that the Trans former, R2Gen and R2Gen which are trained by IU Xray data. Compared with the rest models, which are not fine-tuned in IU Xray, these three models could better learn the reporting styles maintained by the dataset itself, which results in better n- n-gram accuracy measured by NLG metrics.
obtain comparable performances in IU Xray data,
Effect of Controlling Randomness During Alternative Patient Sampling: We provide additional evaluation results by using different selection strategies based on the available metadata in the testing stage. As shown in Table. [0, controlling the randomness during alternative patient sampling beneficial. In future work, we will also address how to achieve controlled sampling for datasets without available metadata.
A.2 MODEL IMPLEMENTATION
We use Swin Transformer (Base) Liu et al. 2021 as visual encoder and Clinical-BERY as lan- guage encoder Yan & Pei 2022 The input images are resized 224 x 224 and split into HW = 49 patches, while the dimension is set to D = 512. The training epoch is set to 40 with the learning rate set to 5e-5 and the batch size set to 64. The maximum length of sentence set to 60 tokens. The important weights setting for i) prototype similarity: a₁ = 0.4. ii) alternative vi- sual proposals (Abb)) and (1lgg); 02 = 0.5, iii) self-critiquing loss L(An)) of alternative proposals 31 = 0.4, and iv) self-critiquing loss L(Neg) of complementary proposals 8g = 0.6.
httpp::/buggee
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CheXbert
CE
RadGraph
RadNLI
Model
Acc.
Abn.
Organ
C.
Re.
F1
IU Xray
Transformer
0.806
0.512
0.169
0.595
0.358
0.281
0.452
0.411
0.396
VLM- based
R2Gen b
0.788
0.418
0.171
0.600
0.306
0.236
0.453
0.408
0.396
R2Gen-CMN
0.820
0.445
0.177
0.612
0.348
0.283
0.486
0.404
0.410
RGRG
0.668
0.459
0.155
0.720
0.340
0.228
0.450
0.431
0.399
Qwen3-VL
0.394
0.099
0.032
0.354
0.129
0.073
0.355
0.220
0.215
MedGamma
0.719
0.310
0.158
0.482
0.186
0.117
0.514
0.222
0.238
LMM- based
LLaVA-Med
0.620
0.282
0.033
0.582
0.215
0.142
0.330
0.341
0.320
LLaVA-Rad
0.801
0.518
0.324
0.717
0.290
0.204
0.326
0.318
0.309
CoMT
0.395
0.135
0.005
0.701
0.284
0.190
0.484
0.337
0.365
BiomedCLIP
0.795
0.381
0.050
0.716
0.315
0.240
0.342
0.422
0.351
BioViL
0.781
0.436
0.268
0.710
0,299
0.209
0.384
0.360
0.345
Retrieval
-based
MedCLIP
0.087
0.092
0.089
0.657
0.164
0.121
0.170
0.215
0.148
CXR RePaiR
0.741
0,385
0.274
0.672
0.258
0.188
0.344
0.339
0.321
X-REM
0.778
0.464
0.255
0.732
0.309
0.233
0.357
0.397
0.350
RadSCR
0.796
0.499
0.366
0.752
0.369
0.300
0.381
0,466
0.390
ReXGradient
Transformer
0.612
0.429
0.081
0.560
0.180
0.111
0.312
0.300
0.296
VLM- based
R2Gen
0.601
0.455
0.090
0.567
0.178
0.117
0.322
0.319
0.310
R2Gen-CMN
0.620
0.450
0.101
0.580
0.190
0.121
0.333
0.311
0.319
RGRG
0,401
0.222
0.095
0.630
0.198
0.135
0.357
0.345
0.335
Qwen3-VL
0.368
0.170
0.060
0.298
0.080
0.047
0.271
0.197
0.163
MedGamma
0.563
0.342
0.174
0.408
0.142
0.092
0.516
0.196
0.217
LMM- based
LLaVA-Med
0.440
0.191
0.098
0.555
0.193
0.129
0.338
0.331
0.318
LLaVA-Rad
0.626
0.478
0.326
0.680
0.228
0.167
0.340
0.313
0.309
CoMT
0.653
0.143
0.665
0.070
0.235
0.653
0.494
0.315
0.349
BioMedCLIP
0.635
0.332
0.215
0.583
0.171
0.137
0.325
0.340
0.318
BioViL
0.529
0.311
0.115
0.632
0.164
0.116
0.359
0.354
0.345
Retrieval
-based
MedCLIP
0.668
0.009
0.259
0.293
0.077
0.028
0.333
0.331
0.331
CXR-RePaiR
0.517
0.357
0.156
0.585
0.192
0.143
0.336
0.333
0.323
X-REM
0.547
0.398
0.157
0.598
0.213
0.167
0.346
0.344
0.332
RadSCR
0.644
0.459
0.344
0.698
0.240
0.179
0.369
0.351
0.356
Table 8: Comparison of report generation by clinical accuracy metrics on IU Xray and ReXGradien data. Models with b tested using the official parameters pre-trained the testing dataset.
MIMIC CXR
X
ReXGradient
Model
B.
M.
R.
B.
M.
R.
B.
M.
R.
Transformer
0.115
0.160
0.287
0.231
0.360
0.402
0.099
0.121
0.192
VLM- based
R2Gen
0.100
0.142
0.282
0.214
0.346
0.383
0.081
0.119
0.199
D2Gen-CMN
0.132
0,210
0.302
0.244
0.398
0.414
0.085
0.212
0.233
RGRG
0.154
0.328
0.365
0.128
0.333
0.380
0.091
0.255
0.264
QWen3-VL.
0.040
0.199
0.142
0.040
0.209
0.140
0.030
0.177
0.111
MedGamma
0.037
0.206
0.144
0.036
0.209
0.140
0.028
0.185
0.118
LMM-
based
LLaVA-Med
0.111
0.231
0.243
0.090
0.213
0.224
0.079
0.220
0.201
LLaVA-Rad
0,206
0.336
0.342
0.141
0.270
0.288
0.138
0.279
0.256
CoMT
0.100
0.290
0.219
0.100
0.333
0.240
0.077
0.263
0,200
BiomedCLIP
0.152
0.266
0.269
0.129
0.258
0.257
0.094
0.197
0.186
BioViL
0.168
0.300
0.289
0.188
0.333
0.317
0.144
0.263
0.241
Retrieval -based
MedCLIP
0.090
0.237
0.168
0.120
0.266
0.233
0.075
0.229
0.163
CXR CeP-RePaiR
0.174
0.312
0.294
0.162
0.295
0.290
0.120
0.230
0.205
X-REM
0.161
0.286
0.291
0.166
0.294
0.309
0.137
0.258
0.226
RadSCR
0.344
0.460
0.452
0.176
0.311
0.312
0.157
0.290
0.259
Table 9: Comparison of report generation by NLG metrics MIMIC CXR, IU Xray and ReXGra- dient data. "B.". "M." and "R." indicates BLEU, METEOR and ROUGE scores.
To obtain the saliency map of the abnormality classification, we use the XGrad-CAM (Fu et al.) 2020 to extract the class activation map from the visual encoder. The bounding box extraction is referred to the open-source code
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CheXbert
CE
RadGraph-Fi
RadNLI
Sampling ACC.
F-1
Abn.
Organ
C.
Re.
MIMIC CXR
0.574
0.610
0.572
0.744
0.422
0.367
0.440
0.433
0.408
PA/A
0.588
0.615
0.566
0.759
0.429
0.370
0.435
0.421
0.399
Posture
0.570
0.613
0.579
0.762
0.427
0.369
0.451
0.429
0.415
ReXGradient
0.644
0.459
0.344
0.698
0,240
0.179
0.369
0.351
0.356
Sex
0.639
0.460
0.347
0.685
0.237
0.171
0.365
0.348
0.352
Age
0.650
0.462
0.351
0.703
0.243
0.175
0.374
0.342
0.350
Table 10: Results of applying controlled sampling in alternative image critiquing.
We use Phi (4B) Ren al. 2025) as the LLM decoder The prompt used for LLM decoding is shown in Fig.
The experiment conducted with an Intel(R) Xeon Gold CPU (2.70GHz) and four sets of NVIDIA Tesla V100S GPU. The training / inference time is reported in Table. IT
Module
Training (s)
Inference (s)
Abnormality Region Localization Self-Critiqued Sentence Retrieval LLM-based Report Generation
0.311
0.193
0.463
0.300
0.203
Table 11: The estimated time of training / inference per image.
N = 37 abnormalities targeted by RadSCR are provided by Chest ImaGenome Wu et al. 2021)) which are annoated on MIMIC CXR data: Low lung volumes, Plerual effusion, Edema, Atelec- tasis, Opacity. Pneumonia, Calcification, Lung cancer, Lesion, Mass/nodule, Costophrenic angle blunting, Consolidation, Aspiration, Hyperaeration, Vascular redistribution, Emphysema, Intersti- tial lung disease, Scarring. Vascular congestion, Pneumothorax, Fluid overload/heart failure Gran- uloma, Lobarlsegmental collapse, Tube/line, Alveolar hemorrhage, Increased reticular markings/ild pattern, Infiltration, Enlargement, Medical device, Pericardial effusion, Mediastinal displacement, Mediastinal widening, Hernia, Tortuous aorta, Spinal degenerative changes, Bone deformity, and Bone fracture.
A.3 EANORMALITY-AWARE RETRIEVAL. REPOSITORY CONSTRUCTION
To retrieve the relevant report sentences for critiquing the visual proposal, we first construct / = 37 sentence repositories of V = 37 abnormalities. For MIMIC CXR data, we annotations provided by the Chest ImaGenome Wu et al. (202T), where each sentence of the report in MIMIC CXR is annotated with the abnormalities and anatomical parts mentioned. For IU Xray and ReXGradient without annotations, we use a BERT-basee text classifier to predict all the abnormalities mentioned and the positive abnormalities described in the report. This text classifier trained by the Chest ImaGenome annotations on MIMIC CXR reports. We collect sentences of the same abnormality into the same sentence repository. Sentences with more than one abnormality mentioned can be collected in multiple repositories. Given that some reports might mention some but not all negative abnormalities, there could be no sentences for some abnormalities be collected. this case, we will supplement the sentences of non-mentioned abnormalities by some simple templates.
A.4 EVALUATION METRIC
CE-Abn covers 37 abnormalities considered by RadSCR. We finetune a text classifier (Sap- BERT Liu et al. 2020 to predict whether these 37 abnormalities are mentioned as positive (ob-
hhttps://giiti
17664e733c/
utils.py.
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(user)
## Instruction
You are assistant specialised in radiology X-ray imaging topics. You are provided with two sets of the diagnostic results expected to summarized them and generate comprehensive radiology report. Below requirements the report generation
""EQUIREMENTS"
There are two sentence sets, one "Primary" set, another is "Secondary" set. All sentences "Primary" set MUST BE USED summarizing, where details expected to be maintained. The repeated content be omitted. The conflicts across the sentences can be removed.
The sentences in "Secondary" set are used for summarizing they are not opposite the content of the sentences in "Primary" set. summarize sentences, the details are expected to be maintained, the repeated content can be omiitedd and conflicts across the sentence can be removed.
The sentence abnormality in radiology domain, indicating the diagnostic target of the following sentence. (Sentence) is a sentence extracted from the radiology report, describing the observation related to (Abnormality). Just output the report directly. NOT add additional explanations or introduce the answer unless you are asked to.
provided with the format "(Abnormality)" "(Sentence)" (Abnormality) the chest-related
*# Example "Primary" set
"omimpll_Dnoommallty,..""" '(Example (Example_Sentencee
Abnarmll_bnoorrmittyyy""""" Eexample_Sentenneee
"Secondary" set
"(Example Abnormalitzz)": Sixrappla_Sentnnceee "(Example Abnormalityz2" "(Example_Sene Sentence
Report to be generated:
"Report": Seetapple__eportt
## Input
"AAborrallty,,)"" "[Sentence])". "Abnormality;p"" "(Sentence
"Seoondary" set
"(Abnormalttya""" ((Sentenezz)" "(Abnormalityae" (Sentenceze)",
Output Report generated:
(assistant)
Figure Prompting data example used for the LLM decoder of the proposed RadSCR.
served on X-ray image) the given report. The labels are annotated Chest ImaGenome Wu et al. (0211.
CE-Organ covers 25 anatomical parts annotated Chest ImaGenome Wu et al. (002I), including: Left lung, Right lung, Left upper lung zone, Right upper lung zone, Left mid lung zone, Right mid lung zone, Left lower lung zone, Right lower lung zone, Left hilar structures, Right hilar structures, Aortic arch, Cardiac silhouette, SVC, Cavoatrial junction, Upper mediastinum, Left costophrenic angle, Right costophrenic angle, Left clavicle, Right clavicle, Left apical zone, Right apical zone, Spine, Trachea, Left hemidiaphragm, and Right hemidiaphragm. We finetune text classifier (Sap-
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Liu et al. (2020) to predict whether there are any positive abnormalities associated with these 25 anatomical parts.
BERT
CheXBerd covers 14 common observations considered in Irvin et al. (2019): Enlarged cardio- mediastinum,, Cardiomegaly, Lesion, Lung opacity, Edema, Consolidation, Pneumonia. Atelectasis, Pneumothorax, Pleural effussion, Lung Other, Fracture, Support devices, and No Findings.
RadGraph covers 14,579 entities and 10,889 relations defined in the related work Jain et al.
2021
RadNL covers the inference relationships Contradiction, Entailment and Neutral.
Preference Order (PO) measures the correctness of 37 abnormalities considered in the RadSCR.
BLUE, METEOE and ROUCE are refer to a public-accessed implementation 14
A.5 BASELINE IMPLEMENTATION
Trans former composes an encoder of three-level transformer layers and a decoder of three-level transformer layers, as implemented by Chen et al. (2020 15
The remaining baselines are implemented according their official codes and praited
weights, including R2Gen Chen et al.
/R2Gen
R2Gen-CMN Chen et al. (2021):
RGRG Tanida et al. (023):
ht
CXR-
RePaiR Endo et al.J (2021):
https:/ but...ttttttttttttt.tuu......... http:e//gataabccc///papaaaaaaaaa
MdeCLIP Wang et al.
(2022
BiomedCLIP Zhang et
al.
BiomedCLIPP- P-ACCCIIPPPPSAA PPMMMLPPPPPMMBBBBR 256-vit_beet
BioViL Boecking
ct
al.
ht22
X-REM
Jeong et al.
(0023
https: http://wwww..ooo/pppppaaaaaaaaaa TDIL
Li
al. (2018) and
HAM
Ouyang et 7:t.00/attaa//gggttbb.ooooo
A.6
EVALUATION ON ABNORMALITY LOCALIZATION
VinDr-CXR Nguyen et al. 2022 2020 16 provides annotations of abnormality regions, including
Infiltration, Lung Opacity, Consolidation. Nodule/Mass, Pulmonary fibrosis, Pleural thickening, Aortic enlargement, Cardiomegaly, ILD, Other lesion, Pleural effusion, Calcification, Enlarged PA, Lung cavity, Atelectasis, Mediastinal shift, Lung cyst, Pneumothorax, Emphysema, Clavicle frac- ture, Rib fracture, and Edema.
ChestXray8 Wang et al. 2017 provides annotations of abnormality regions, including Atelectasis, Cardiomegaly, Pleural effusion, Infiltrate, Mass, Nodule, Pneumonia, and Pneumothorax.
We evaluated the localization results of the abnormalities shared across MIMIC CXR (Chest Im- aGenome), VinDr-CXR and ChestXray8. We noted that the localization annotations of the same abnormality from different datasets could be variable to some extent, as the localization results of radiologists could be affected by population differences, local operating rules, or personal experi- ences. However, for common chest abnormalities, their relevant regions to localized by different radiologists should be similar in most cases, as the related diagnostic consensus for these abnormal- ities has been studied for years.
httas://iimdr.al/dassstttt/////
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A.7 VISUALIZATION OF ABNORMALITY LOCALIZATION
We present two cases of progressive change of saliency maps with abnormality region localized by RadSCR during the training process (as shown in Fig. 4 and Fig. 5). As observed, trained RadSCR could localize relevant regions of the abnormalities presented. Meanwhile, the localized regions also covers some irrelevant areas, which indicates that the weakly-supervised abnormality localization is still challenging.
A.8 LATENT SPACE VISUALIZATION
We present the visualization of concept embedding, prototype embedding, and sentence embedding of randomly sampled sentence sets learned with and without the proposed self-critiquing mechanism (as shown Fig. and Fig. 7 For each abnormality in every setting (with without the proposed present the visualization of one concept embedding, AC 5 proto- type embeddings, and 100 randomly sampled sentence embeddings of which sentences are associ- ated with each kth prototype (in total 500 sentence embeddings for each abnormality). We use the t-SNE algorithm to project the D-dimensional embeddings into 2-dimensional vector. In general speaking, the points of embeddings learned with the self-critiquing mechanism are gathered more than those without the self-critiquing mechanism in most plots. It might indicate that the critiqued embeddings could represent the related information of each abnormality with less irrelevant features. However, this kind of visualization is also affected by the dimension reduction algorithm we use, while it is not the only way to explain these visualization results. We also note that how to properly interpret the learned representation in latent space remains open.
self-critiquing mechanism),
A.9 TRENDS OF SENTENCE SIMILAR SCORES THE VISUAL PROPOSAL AND THE ALTERNATIVES DURING TRAINING
The proposed RadSCR allows the visual proposal Pm and the alternatives (ClAhhn) C.(lung) interact by contrasting their similarity scores with the sentence 9(m) to compute a discounted similarity
score ((P,, (im) (according Eq. 11). In addition, ((Neg) further supplements Pn, recover false negatives. As shown in Figure 8, during training, the similarity score between Pm and 8(m)
increases while the scores between (Abbb)) _.llm))) and ((m) decrease, as anticipated. In addition, the similarity score between false negatives (Neg) and the corresponding sentences s(o) increases, so that the learned model can detect missing abnormalities.
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Ground truth Report
Lungs hyperinfiated with fisttening of the diaphragme and increased AP diameter suggestive COPD.
Minor left base atelectasis //arrin is seen. No definite focal consolidation is seen. There is no pleural effusion or pneumathorax. Cardiac silhouette is top-normal to mildly enlarged. The aorta is calcified.
The image shows chest X-ray. The heart appears to be slightly enlarged (cardiomegaly). The cardiomediastina silhouette remarkable acrlic arch calcifications. Further evaluation with echocardiography would be needed determine the cause. There fracture is rederonstraated. The lungs hyperinflated. Mild atelectasis is seen in the lung bases without focal consolidation. The mediastinum is clear. The mediastinal and hilar contours are unremarkable. No free air below the right hernidaaphragm seen. Mediastinal contours are normal with mild aortic lortupsity.
Generated Report
pleural effusions pneumothorax. The right sixth rib surgical
Positive
Falargament, Calelliction, Atslectasis, Hypersazation
Posttive
Tomuous Aona
Magatiue
Seaning
epoch
opoch
10 opoch
20 epoch
40 opoch
Reference Annotation
Hyperseratonn ration
Atolectable
Scarring
Enlargement
Culeification
Figure 4: Illustration of progressive changing of saliency maps with abnormality localizations dur- ing the training process of RadSCR (Case I). The reference annotation of the abnormality regions (bounding boxes within green lines) are also provided which are inferred from the paired report.
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Ground- truth Report
Slight interval increase the dagree retrocardiac airspace upacily, which may represent worsening atelectasis consolidation. The right lung grossly clear. No pleural effusion or pneumothorax. The cardiomediastinal silhouette is unchanged
PA and lateral views of the chest. The heart is normal position, and there is no evidence of cardiomegaly, The lungs are clear of focal ponsolidation, effusion, or pulmonary vascular congestion. In comparison with the study of there has been interval improvement in left lower lobe atelectasis. There is no pulmonary edema. There is no preumothorax or pleural effusion. The aortic arch is not opacified. The left lung has small opacity, which may 'epresent chronic process. There evidence of old fractures, particularly the left side. The mediastinal and hilar contours are normal.
Generated Report
True Positive Opacity, Atolectas's, Hyperaeration
False Positive
Fracture
False Negative
Consolidation
epoch
Opaolty
Atelectoels
Consolldation
Figure 5: Illustration of progressive changing of saliency maps with abnormality localizations during the training progress of RadSCR (Case II).
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Published as a conference paper at ICLR 2026
Aspiration
Atelectasis
Alveolar Hemorrhage
Bone Deformity
Costophrenic Angle Blunting
Bone Fracture
Calcification
Consolidation
Fluid Overload Heart Failure
Edema
Enlargement
Emphysema
Granuloma
Increased Reticular Markings Pattern
Hernia
Hyperaeration
Interstitial Lung Disease
Infiltration
Lesion
Lobar / Segmental Collapse
Figure 6: Visualization of concept embedding, prototype embedding and sentence embeddings in the latent space (Part I).
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Published as a conference paper at ICLR 2026
Low Lung Volumes
Lung Cancer
Lung Opacity
Mass Nodule
Mediastinal Displacement
Medical Device
Mediastinal Widening
Pericardial Effusion
Pleural Effusion
Pneumonia
Pneumothorax
Scarring
Spinal Degenerative Changes
Tube / Line
Vascular Congestion
Tortuous Aorta
Concept Embedding
Prototype Embedding
Sentence Embedding
w/o. Critique
w/. Critiqued
Vascular Redistribution
Figure 7: Visualization of concept embedding, prototype embedding and sentence embeddings in the latent space (Part II).
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Published as a conference paper at ICLR 2026
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d(Pms (mm) @(Pms (m))
a( (m)) o oC (Img)
((m) (C ,()))
Figure 8: Trends of the sentence similar scores of the visual proposal and the alternatives during training. oPnn {(m)), a(C..bb)) .(mm)), a(C.lmg) (())., and (N s(o)) are the similarity scores defined in Section 3.3, and @(Pm. 8(m)) is the overall discounted score.
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Reviews:
[{"review_id": "KzxbMUgrLl", "reviewer": "Reviewer_sWJS", "summary": "This paper introduces RadSCR (Radiology Self-Critiquing Reporting), a region-guided framework for chest X-ray report generation that learns self-critiquing mechanisms end-to-end—without relying on costly test-time reasoning. It mimics radiologists’ diagnostic verification through three critiques: (1) comparing predicted abnormalities against alternative ones, (2) testing patient-specificity via other patients’ images, and (3) checking for potential false negatives. Validated region proposals are then used for retrieval-based sentence generation with an LLM decoder. RadSCR significantly outperforms state-of-the-art methods on MIMIC-CXR and related datasets in both clinical accuracy and abnormality localization, offering interpretable and reliable radiology reports.", "questions": "1. In Alternative Abnormalities Critiquing, the paper states that the alternative embedding $E’_m$ is chosen from negatively predicted abnormalities or positive ones in non-overlapping regions. Have you examined whether “hard” (visually similar) versus “easy” (semantically distant) alternatives affect training stability or discriminative power?\n2. Sampling Strategy for Alternative Patients\nThe paper describes replacing the current image feature V with that of “a randomly selected other patient.” How is randomness controlled here, do you ensure the same projection view (PA/AP) or similar patient metadata to avoid distribution drift?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The paper presents a highly original motivation inspired by the self-critiquing process of radiologists, bridging human diagnostic reasoning and machine learning. Instead of relying on expensive LLM-based reasoning, the authors design learnable self-critiquing mechanisms (alternative abnormalities, alternative patients, and false negative checking) that elegantly emulate how clinicians verify findings before writing reports.\n\nThe technical quality is strong: the proposed framework integrates region grounding, self-reflection, and retrieval-based generation into an end-to-end trainable architecture, supported by comprehensive experiments across multiple datasets.\n\nIn terms of clarity, the paper is well structured, with intuitive explanations, clear equations, and visual schematics that make the workflow easy to follow. The significance is substantial for both the medical AI and vision-language communities. It provides a scalable way to enhance interpretability and reliability in clinical report generation without the overhead of LLM reasoning. The self-critiquing paradigm could inspire broader applications in trustworthy generative modeling beyond radiology.", "weaknesses": "The main limitation of this paper lies in the design rationale and justification of the alternative feature mechanisms that form the foundation of the proposed self-critiquing framework. While the idea of emulating radiologists’ reasoning through alternative hypotheses is conceptually appealing, the paper does not sufficiently explain why these particular forms of “alternatives” (abnormality-level, inter-patient, and false-negative critiques)were selected, nor how they were parameterized or sampled during training.\n\nFor example, in Alternative Abnormalities Critiquing, the paper describes selecting an alternative abnormality embedding $E’_m$ from either negatively predicted classes or non-overlapping positive predictions. However, it remains unclear how this selection impacts the discriminative strength of the critiqu, whether the alternatives are chosen to be visually similar, semantically distinct, or dynamically learned during training. Without such clarification, it is difficult to assess the robustness, interpretability, and reproducibility of the proposed mechanism.\n\nIn Alternative Patients’ Images Critiquing, the model substitutes the visual feature V with that of a “randomly selected” patient. While this offers cross-patient contrast, it may introduce semantic and anatomical misalignment, as random images can vary significantly in projection type (PA vs. AP), patient posture, or imaging equipment characteristics. A more controlled sampling strategy (such as selecting patients with comparable acquisition parameters or anatomical embeddings) could yield more meaningful contrastive feedback. An analysis of the sensitivity of this mechanism to inter-patient variability would strengthen the empirical validity.\n\nRegarding Potential False Negatives Critiquing, the current implementation averages embeddings of all predicted negatives into a single global representation $E_0$. This coarse aggregation may dilute subtle abnormalities and risks introducing false positives due to noisy global pooling. It is unclear how this global feature contributes to identifying missed abnormalities or whether it merely adds uninformative signals. A more structured approach, such as region-aware aggregation or uncertainty-guided negative sampling, might enable more precise critique and localization.\n\nConceptually, the idea of constructing contrastive or counterfactual pairs to improve visual-language grounding is not entirely novel. Prior work, such as [1] shares a similar objective of forming counterfactual pairs to enhance discriminative representation learning and reduce hallucination. The present paper would be strengthened by explicitly discussing this connection. To clarify how RadSCR’s self-critiquing differs conceptually and methodologically from counterfactual reasoning, what additional benefits it introduces, and how the two paradigms could complement each other.\n\n[1] Li, M., Lin, H., Qiu, L., Liang, X., Chen, L., Elsaddik, A., & Chang, X. (2024, ECCV). Contrastive Learning with Counterfactual Explanations for Radiology Report Generation.", "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": "tq2CxtLWr3", "reviewer": "Reviewer_XBBS", "summary": "This work presents RadSCR, a new methodology for report generation on chest x-ray images using self-critiquing mechanisms. The method utilizes a triplet to retrieve text descriptions for the abnormalities detected by the visual model. This triplet comprises: the image representation obtained using a vision encoder; local information provided by Grad-CAM; and learned concept embeddings for each abnormality. The framework is trained end-to-end to align image and text embeddings using contrastive losses. The final report is generated by post- processing the retrieved phrases using an LLM. The results show that RadSCR not only surpasses the state-of-the-art for report generation but also improves the capabilities of the visual model to detect abnormalities.", "questions": "· In section 3.3, what is usually the sentence length l for the s_bert variable? Since this variable was created by concatenating all sentences for the same abnormality, its length raises concerns regarding implementation.\n\n· When computing the metrics, are the ground truth annotations used as targets? Or the predictions for the visual model? Both cases would offer valuable information for the reader, since in the first one, it checks how the report matches the ground truth, and in the\nsecond, how well it describes the visual model predictions.\n\n· Regarding the inference, is the search space for the sentences only among the subsets of a given abnormality? For instance, if the model predicted abnormality A as present, is the search space for the sentences only the set of sentences that describe A, or are all the\nsentences in the dataset? This would have implications for the retrieval results, since the first case would be easier.\n\n· The authors mentioned the lack of localization for the false negatives as a limitation. Nevertheless, it is possible to apply Grad-CAM to the abnormalities that are not predicted. Did the authors try to use this information to estimate the negative box B0 better and overcome this problem?\n\nMinor Comments:\nIn the related works, what do the authors mean when it is said that localizing anatomical parts is not precise enough for grounding?\nSome misspellings in the text:\n- Line 63: triplet is misspelt\n- Line 224: detection is misspelt\n- Line 258: replace production by product\n- Line 305: positive sample and negative sample are misspelt\n- Line 323, 326: approaches is misspelt\n- Line 338: replace “by the following metrics” with “with the following metrics”\n- Line 341: which also considers is also misspelt\n- The term “in the sequel” appears in the text; it is recommended to replace it with the term “in\nthe following section” or “below”.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "· Presented a novel and sound methodology to combine concept learning with local information provided by post-hoc xAI techniques (Grad-CAM in this case) to improve report generation.\n· Extensive comparison between RadSCR and the state-of-the-art methods across different metrics and datasets, showing significant improvements for report generation.\n· Showed that using RadSCR on top of a visual encoder improved the abnormality detection performance when compared to an MLP.\n· Performed an ablation study that gives more insights about how each part of the proposed method impacts the overall performance.", "weaknesses": "· Figure 1 should be improved; it does not give the reader a broad idea of how the method works.\n· Some details about inference are hard to grasp, which raises concerns regarding the performance evaluation of the retrieval results. A schematic would benefit the understanding of the inference process.\n· More details about the Clinical Efficacy metrics are missing. The target used for some of the metrics is also not clear.\n· Even though the authors briefly discuss the work's limitations, they are unclear to the reader. That section should also be extended with further discussions.", "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": "PuWwERJ1x4", "reviewer": "Reviewer_MuJA", "summary": "In this paper, the authors present an improved approach to report generation utilizing the critique principle. The basic idea is to form a triplet (V,B,E) representing the visual information in a region V, bounding box B, and concept E (presumably the finding itself), and contrasting triplets (V',B',E') representing variations due to substitutions with incorrect findings, incorrect locations, and image content replacements to indicate if any other possibilities exist other than the correct triple (V,B,E). The actual report generation is then done by retrieving the relevant sentences using these triplets as keys and assembling the overall report.", "questions": "What happens if the above operations in critique generation overlap and produced redundant triplets?\nLine 236 says all sentences with the same finding are concatenated. How does that help? Wouldn’t it make the encoding worse in representing the finding with a mixture of sentences?", "limitations": "", "rating": 2, "confidence": 2, "soundness": 3, "presentation": 1, "contribution": 2, "strengths": "The idea of setting up contrasts with incorrect placements and incorrect findings or image appearances that can indicate two different findings is in general a good idea. The results on standard datasets appear to outperform comparables on the dimensions analyzed which include regional grounding performance as well as accuracy of findings classification.", "weaknesses": "The description of the method could be improved by giving examples of each case to make it convincing. The critique principle should be explained clearly. What is the model architecture? Its inputs and outputs are not clearly explained. \n\nOverall this idea is similar to the one proposed in the recent paper on phrase-grounded fact-checking(Ref1) where the idea of synthetic data generation also explored real and fake finding cases but built instead a discriminative classifier to separate the findings. While that paper proposed it for inference-time checking, this paper is proposing a similar one for report generation itself. The number of findings examined is much more limited in this case, so the performance improvements perhaps cannot be compared. \n\nComparison with newer models needs to be done ( GPT4o, XRAYGPT and many other representative decoder models including MAIRA-2).\n\n1. \"Phrase-grounded Fact-checking for Automatically Generated Chest X-ray Reports\",  https://papers.miccai.org/miccai-2025/paper/3526_paper.pdf", "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": "ChaN8LIbU5", "reviewer": "Reviewer_8xBL", "summary": "This paper proposes a self-criticism mechanism learning framework (RadSCR) for chest X-ray report generation, aiming to improve the clinical accuracy and interpretability of report localization. Unlike traditional models that rely solely on visual-text correlation training, RadSCR simulates the \"self-reflection\" behavior of radiologists during the diagnostic process, re-evaluating initially detected abnormal areas through three complementary mechanisms: based on surrogate anomalies, surrogate patient images, and critical verification of potential missed detections. The model employs a weakly supervised localization and retrieval-based report generation paradigm, embedding the self-criticism mechanism into the network structure to achieve end-to-end training, invoking the LLM inference chain when inference is not required.", "questions": "1.\tWill the three critique mechanisms (surrogate anomalies, surrogate patients, and potential missed detections) in the paper adaptively adjust their importance or weights during training through model learning? If there are interactions between the mechanisms, can these relationships or independent contributions be quantified?\n2.\tWhile the three critique mechanisms have shown advantages in experiments, can their gains be validated through further ablation experiments, especially regarding the impact of different critique signals on performance?\n3.\tCurrently, the model's self-criticism only functions during the training phase. Has some form of \"dynamic self-correction\" mechanism been considered for the inference phase to address new error types or unseen biases?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "This paper focuses on the core clinical behavior of \"self-correction,\" transforming it into a learnable critical mechanism within the model. Methodologically, it combines multi-source contrastive learning with retrieval-based generation, improving anomaly localization accuracy while enhancing semantic alignment. Experiments cover three tasks (report generation, retrieval, and localization), providing comprehensive validation with reasonable indicator selection.", "weaknesses": "While the paper's overall design is clear and the results are convincing, there remains some ambiguity between the concept and implementation of its \"self-criticism mechanism.\" The three current critical paths (substitute anomaly, substitute patient, and potential missed detection) are more like structured perturbations of the input space than a rigorous \"self-reflection\" process. Therefore, the model's critical learning ability still relies on a pre-defined data augmentation paradigm rather than genuine adaptive induction from errors. Furthermore, although these mechanisms appear complementary in the experiments, the paper fails to systematically reveal their interactions and lacks quantitative analysis of the relative contributions or sensitivities of different critical signals. This makes \"multidimensional criticism\" theoretically more like parallel regularization than organic collaboration.", "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)