"Why Is There a Tumor?": Tell Me the Reason, Show Me the Evidence
Authors: Mengmeng Ma
Tang Li
Yunxiang Peng
Lu Lin
Volkan Beylergil
Binsheng Zhao
Oguz Akin
Xi Peng
Venue: ICML 2025 poster (2025)
Keywords: Trustworthy Medical Image Analysis
Primary area: applications->health_medicine

Abstract: Medical AI models excel at tumor detection and segmentation. However, their latent representations often lack explicit ties to clinical semantics, producing outputs less trusted in clinical practice. Most of the existing models generate either segmentation masks/labels (localizing where without why) or textual justifications (explaining why without where), failing to ground clinical concepts in spatially localized evidence. To bridge this gap, we propose to develop models that can justify the segmentation or detection using clinically relevant terms and point to visual evidence. We address two core challenges: First, we curate a rationale dataset to tackle the lack of paired images, annotations, and textual rationales for training. The dataset includes 180K image-mask-rationale triples with quality evaluated by expert radiologists. Second, we design rationale-informed optimization that disentangles and localizes fine-grained clinical concepts in a self-supervised manner without requiring pixel-level concept annotations. Experiments across medical benchmarks show our model demonstrates superior performance in segmentation, detection, and beyond. The anonymous link to our code.

"Why Is There a Tumor?": Tell Me the Reason, Show Me the Evidence
Mengmeng Ma
Tang Li"
Yunxiang Peng'
Lu Lin Xi Peng'
Volkan Beylergil Binsheng Zhao
Oguz Akin
Abstract
Prediction Rationales
Medical AI models excel tumor detection and segmentation. However, their latent representa- tions often lack explicit ties to clinical semantics, producing outputs less trusted in clinical practice. Most of the existing models generate either seg- mentation masks/labels (localizing where with- out why) or textual justifications (explaining why without where), failing to ground clinical concepts in spatially localized evidence. To bridge this gap, we propose to develop models that can justify the segmentation or detection using elinically rel- evant terms and point to visual evidence. We address two core challenges: First, we curate a ra- tionale dataset to tackle the lack of paired images, annotations, and textual rationales for training. The dataset includes 180K maage-mask-rationall triples with quality evaluated by expert radiol- ogists. Second, we design rationale-informed optimization that disentangles and localizes fine- grained clinical concepts in self-supervised man- ner without requiring pixel-level concept annota- tions. Experiments across medical benchmarks show model demonstrates superior perfor- mance in segmentation, detection, and beyond. The code is available at https//github.com/dee. real/MedRationale.
Methods
Predictions
Clinical Concepts
Visual Evidence
UNet
Seg. Model
Seg. Mask
x
SAMed
GradCAM
X
X
Category-level
XML
Concept Bottleneck
Category
X
MLLM
OpenAl o1
Category
Ours
Seg. Mask
Concept-level
In contrast existing methods, our proposed method not only conducts accurate predictions, but can also provide its prediction rationales using clinical concepts supported by valid visual evidence.
Figure 1:
evidence, guiding clinical action. AI is increasingly inte- grated into this workflow, assisting radiologists in detecting and segmenting tumors (Ozer et al., 2010; Zhao et al., 2024). Data-driven methods (Ronneberger et al., 2015; Isensee et al., 2021) enable AI models to achieve remarkable ac- curacy. However, most existing models process images via statistical pattern recognition, mapping pixcls to labels through latent representations that lack explicit ties to med- ical semantics. Consequently, these models often produce outputs that lack clinical interpretability. For instance, they can localize anomalies using segmentation masks without explaining why regions are suspicious. This raises a criti- cal question: Can Al models justify their prediction using clinical terms and point visual evidence?
1. Introduction
Magnetic Resonance Imaging (MRI) interpretation work- flow typically begins with analyzing anatomical and fune- tional sequences identify deviated visual patterns. These patterns are then mapped to standardized clinical lexicons, ensuring consistency in reporting. Structured reports ex- plicitly link these clinical terms to corresponding visual
There are attempts to enhance models to perform beyond segmentation and detection, yet they often fall short of clini- cal needs. The large-language models trained on radiology reports (Yang et al., 2024; et al., 2024a) can generate textual justifications for their diagnosis, but these explana- tions often lack spatial grounding, making difficult for radiologists verify whether key findings correspond to the correct anatomical regions. Similarly, concept bottleneck models (Gao et al., 2024; Patrício et al., 2023) can justify the prediction using clinical concepts, yet fail at associating the concepts with correct pixels (Margeloiu et al., 2021). Conversely, models focused on visual localization such as
'Department of Computer & Information Sciences, Univer- sity of Delaware, Newark, DE, USA 'Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA. Correspondence to: Xi Peng <xipeng @ edul.
Proceedings of the ndnd International Conference Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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"Why Is There a Tumor?": Tell Me the Reason, Show Me the Evidence
those employing salieney maps (Selvaraju et al., 2020) can highlight pixels driving predictions, yet they lack explieit clinical semantics, offering no textual rationale to explain why region appears suspicious. Vision-languaae models pretrained on image-text pairs enable linking global im- age features with captions but struggle to disentangle fine- grained concepts, such as distinguishing "capsular invasion" from "benign hyperplasia" within the same gland.
Definition 2.2 (Visual evidence). Let X be an impud image with ground truth label y E y. The valid visual evidence for es is the subset of pixels in æ that are most relevant to Ck- Let St C IR² denote the pixel space, and let 9 : X x C - -2 map (x, cx) to ((x, (x) C Q. Then, [ ox) is the visual evidence for ck-
Taking prostate tumor diagnosis as example, the ratio- nales could be language descriptions of established clinical concepts {(+} (e.g., hypointense signal intensity, circum- scribed margins, or lenticular shape), where each clinical concept e₁ should be supported by corresponding image regions ((r, cx) to ensure visual validity.
Developing models that justify predictions and point to visual evidence faces two major challenges: (1) Data Chal- lenge: Training such models typically require paired predic- tions and rationales, but the paired data are largely missing. Most existing public medical benchmarks (Saha et al., 2024; Wang et al., 2017; de Verdier et al., 2024) provide imaging data and lesions or anatomy annotations but lack the cor- responding textual rationales. (2) Optimization challenge: Grounding clinical terms to visual evidence requires fine- grained image-concept alignment at the pixel level. Manual labeling of suncept-pixels pairs is prohibitively expensive, especially for medical images. This raises the challenge of optimizing the model for concepts localizing without having the ground truth label.
Given a data point (zz, y) drawn from distribution P(X, Let f E FF be a predictive model, E be a task-specific loss, and h((-) be a method that maps ct to a region in x depend- ing on f. We propose to solve the following optimization problem to obtain a rationale model:
min R()) (1 JEF
(1)
s.t. hx; g c4), Vck E C.
Solving the problem is non-trivial, as both the concepts {c*} and (.) are not available. In medical image analysis, some efforts have been made to provide textual rationales along with corresponding pixel-level annotations (Tschandl et al., 2018; Daneshjou et al., 2022; Bannur et al., 2024). However, these approaches are typically constrained to small-scale datasets and are impractical for large-scale applications due to the high cost of fine-grained annotations. To address this challenge, we present our approach to automatically obtain the concepts in Section and demonstrate how to train a rationale model without access g(-)
To bridge the data gap, we curate first-of-its its-kind rationale dataset, providing 180K high-quality textual justifications for 1.5K MRI scans. Developed in collaboration with six radiologists over 150+ hours of structured annotation, each rationale captures the detailed reasoning process behind clinical assessments. To ensure reliability, rationales under- went dual-expert validation across key metrics such as fac- tual accuracy and comprehensiveness. Unlike conventional datasets (Adams et al., 2022; Saha et al., 2024) limited to image-mask pairs, our work pioneers image-mask rattionnl triples, explicitly linking raw imaging data, expert annota- tions, and the underlying clinical logic.
3. Rationale Dataset Curation
To fill the optimization gap, we design rationale-iformmed optimization, a supervied framework that disentangles and localizes clinical concepts without pixel-level annota- tions. We evaluate the proposed method on multiple medical benchmarks. For segmentation and detection, our method achieves +8.8% DSC and +5.3% AP over the state-of-the-aar models. For rationale correctness, our model significantly improves visual evidence localization, achieving 0.403 Rele- vant Mass Accuracy (RMA T) (Arras et al., 2022) compared to the baseline's 0.010 RMA.
This section describes our strategies to obtain the textual description of "why" and "how" behind experts' reasoning steps, referred to as rationale data. Using prostate cancer (PCa) as an example (Sekhoacha et al., 2022), we describe how to: represent domain knowledge in structured format, source rationale data, convert it into Al-ready annotations using domain knowledge, and ensure data quality. Impor- tantly, our data curation procedures are adaptable to other cancers, such as breast (Cozzi et al., 2024). lung (Mehta et al., 2017), and liver (Chernyak et al., 2018)
How represent domain knowledge? To systematically capture the why and how behind Prostate Imaging Reporting and Data System (PI-RADS) scoring, formalize expert reasoning into PI-RADS Decision Tree (PDT). Developed with radiologists and aligned with PI-RADS v2.1 guidelines, the PDT maps multi-step clinical logic into a traceable hier- archy (Figure 2(a)). Each node represents a reasoning step, ensuring structured, auditable rationale generation.
2. Problem Formulation
In this section, we provide our definition of rationale and the problem formulation.
Definition 2.1 (Rationale). We define rationales to be a set of abstract concepts {c&} C. C that can sufficiently justify a prediction, supported by valid visual evidence.
2

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"Why Is There a Tumor?": Tell Me the Reason, Show Me the Evidence
(a) PI-RADS Decision Tree (PDT)
(b) PI-RADS Report
(c) GPT-4o Extracted Rationale without PDT Augmentation
PIRADS Guideines
Lesion Location
FINOWNSS Lesion
The lesion has size of 1.6 0.7 cm. location the right midgland lta lion zone with presercce eddaccapular eclenssoon Theee eggr essive charactetistis suggest signtficntt PCa, thus leacing to PI-RADS scare
Locators right midg lond
Lason TZ
Lesion in PZ
transition zone antortor.
Size: 8
TZ. lssion should check T2W MRI
Check ADC/DWI
Expacapeular extension:
(d) GPT-4o Extracted Rationale with PDT Augmentation
present doftrite invasion...
The identified lasion situated the anterior segment In T2W, we tranzition zona: Imporative frrst chack 12W
right midgaand
T2W show typical nodule
T2W show ... homo. Hypointenze...
Seminal vasiclo invasion:
Adjacani organ invasion....
abserve hemogeneous, moderataly low signal intensity along with evidonce extraprostati extensinn Consequently, the lesion is assigned T2W soore Given the lesion hes largest dimension larger then 1.5 on and with extraprostatic exiension. classified PI-RAD8
Score
T2W score is
No abnormality
ENDINGS: Lesion #2:
PHRDS score:
In-conteet Leariin
PLRADS score 5
the structured representation of domain knowledge. PI-RADS report: sources that provide rationale data. Extracted radiologist rationales with and without PDT. Text highlighted in gray represents information existing in the clinical PI-RADS report. Text highlighted in green indicates augmented information by PDT.
Figure 2: Overview of rationale data curation. PI-RADS Decision Tree:
Where to source the rationale data? Clinicians routinely document their observations, interpretations, and profes- sional judgments in clinical reports, providing a valuable source to obtain the reasoning process behind medical de- cisions. For example, reports from the Prostate Imaging Reporting and Data System (PI-RADS) synthesize radiolo- gists' findings, including tumor size, location, MRI signal characteristics, and the likelihood of clinically significant cancer (PI-RADS category: see Figure 2(b)). However, public data repositories (Saha et al.. 2024; Adams et al., 2022; Fedorov et al., 2018; Nguyen et al., 2022; Antonelli et al., 2022) typically provide medical imaging scans and annotations but without accompanying clinical reports.
ate reasoning steps. In contrast, the PDT-augmented ra- tionale contains detailed reasoning steps left implicit in the clinical PI-RADS reports. These findings validate the effectiveness of PDT in enabling GPT-4o to generate ratio- nales that remain within factually valid boundaries while effectively capturing the radiologists' detailed reasoning steps. This also aligns with recent broader research findings that confirm GPT-4o's expertise at expert level in both commonsense reasoning and specialized medical domain knowledge (Bubeck et al., 2023; Liu et al., 2023).
Rationale dataset and statistics. Leveraging the proposed automatic method, we are able to extract rationales from all annotated clinical PI-RADS reports, leading to the first ratio- nale dataset for prostate cancer. The dataset contains 1,500 3D triples (MRI sequence, masks, rationales), decomposed into 36,000 2D triples (MR image, mask, rationale). To further enhance the dataset scale, we generated five GPT-4o paraphrases per rationale, yielding 180,000 total 2D triples.
To bridge this gap, collaborated with radiologists to annotate the public data to create MRI and PI-RADS re- port pairs. We use 1,500 prostate MRI cases from PI-CAI challenge (Saha et al., 2024). Each case includes three MRI sequences: T2W, ADC, and DWI. These sequences have already been annotated with gland, zone, and lesion segmentation masks. For 408 tumor-positive cases, MSK radiologists assess the MRIs and segmentation masks to generate PI-RADS reports.
Can we trust the extracted rationale? We conduct human evaluations of the extracted rationale. The experiment de- tails and metrics that our rationale data are coherent, consistent, comprehen- sive, and highly factual accurate.
described in Section 5.2. Results show
How to extract rationale data efficiently? The variabil- ity inherent in clinical PI-RADS reports could potentially lead to the extraction of incomplete radiologist rationale, often lacking intermediate reasoning steps. We propose to automatically extract detailed rationales from the PI-RADS reports leveraging GPT-4o (Achiam et al., 2023) and PDT. As illustrated in Figure 2(a), we will input 10 example pairs consisting of PI-RADS reports alongside corresponding gold-standard rationales (detailed explanations provided by the radiologist) into GPT-4o for in-context learning (Dong et al., 2022). Specifically, GPT-4o will utilize these exam- ples to learn how to apply PDT and augment PI-RADS reports by filling in the missing reasoning steps. Our full prompt is available in Appendix C.
4. Rationale-Informed Optimization
In this paper, rationale model should be able link textual semanties (clinical concepts) with visual patterns (anatom- ical regions), calling for a shared space where similarity reflects semantic relevance. A straightforward approach is to use the image-text pairs from our rationale dataset and contrastive learning (Radford et al., 2021) to learn a shared vasion-language embedding space.
Main objective. Given an image-text-label triple U,T,M) sampled from P(I,T.M), model ʃ (fimgs fut, fmask) E F that generate image fing and text fiu(T) embeddings, as well as segmentation masks M = Smace(Jing (limg()). Let bueg be the Dice loss (Saha et al.,
The results in Figures 2(c) and 2(d) show that without PDT augmentation, the extracted rationale missing intermedi-
3

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"Why Is There Tumor?": Tell Me the Reason, Show Me the Evidence
Rationale Dalaset
Rationale-Informed Optimization
Segmentations (Sec. 5.3)
Rationales: Clinical Concepts + Visual Evidence (Sec. 5.4)
(Sec. 3)
(Sec. 4)
mp-MRIs
Image Encoder
Mask Decoder
LE
Lesion #1
DWI
T2V
fing
f mask
Lesion is in PZ and TZ
Zone
For the TZ lesion, T2W is the primary modality.
Image-Ted Text
Embedding Space
Radiologist
Disen
T2W shows non-circumscribed. homogeneous, moderate hypointense
Rationales
Gland
Encoder
fixt
Align.
T2W score is
"Lesion"
PI-RADS assessment category is
Figure 3: Overview of the proposed method. Rationale Dataset: In Section 3, we curate first-of-its sit-offitts-inng kind dataset for prostate tumor segmentation that offers textual rationales annotated by expert radiologists. Rationale- Informeldd Optimization: In Section 4, we propose a new optimization method that enables the model to localize the visual evidence of concepts without manual annotations. Segmentations: Our model can provide segmentation masks for Lesions, Zones, and Gland, Rationales: Our model can provide its prediction rationales using clinical concepts based valid visual evidence.
2024) and falign be the InfoNCE loss (Oord et al., 2018). We augment Equation I objective with the additional objective of learning a shared embedding space:
solutions, where heatmaps of different concepts highlight non-overlapping but random regions in the image.
Localization constraint. To avoid trivial solution, we introduce additional localizationi constrain. Our idea is that different concepts describing the same anatomical structure should have heatmaps highlighting the same region. Our full objective is as follows:
min R(J) BU.T,MM (oreg (mmae (J)) M)] SEF
(2)
B(,T,, [aalign (fung (I), (T))].
We conducted training using Equation learn a shared vision-language space and found that incorporating addi- tional text data improves the model's segmentation and can- detection performance (see results in Table 1). The model also demonstrates better zero-shot generalization performance compared with image-only models (Table 2). However, optimizing this equation alone can not guarantee accurate localization of the concepts (Figure 7).
min R(F) s.t. D(h(z, cwi f), h(x, fiff)) E1, JEF
(4)
D Mxx f), S 62
To simplify the optimization, we leverage the KKT condi- tion (Boyd, 2004; Wright, 2006; Qiao & Peng, 2023) and Lagrange multipliers to convert this constraint optimization problem into the unconstrained problem.
Disentanglement constraint. To accurately localize the concept, propose disentangle the concept's heatmap. In the shared embedding space, one can link a concept Ck to its most relevant pixels by calculating a heatmap Ma, Cas f) that compare the similarity between image and text embeddings: fimg(!) H xW xD foo(c)) E R. Ma, ck: f)
Inference. At test time, the model generates rationales by traversing over the PI-RADS decision tree (Figure 2(a)) conditioned on the shared embedding space. We first en- code image into latent embedding fimg(1), For each tree node (c.g., lesion presence, location, margins), we re- trieve the rationale with the highest similarity to fimg(r): argmax mean(Sing
f(a(c))") )), where upsampel(-) resizes the heatmap match the input image resolution. Our insight is that clinically different concepts should highlight different regions in the image. This leads to the following disentanglement constraint:
ppsample(fungle
fix(e))). Note that sub- sequent nodes condition on prior selections. For example, MRI signal characteristics inferred only after lesion location is determined, as signal patterns vary anatomically.
5. Experiments
min R(f) s.t. D(h(x, os fer
(fllr, f), dei f)) E1.
5.1. Datasets, Baselines, Metrics, and Implementation
Where .). )) distance metric such as L2 distance. How- ever, naively optimizing the Equation could lead trivial
Datasets. We conduct experiments on our curated rationale dataset and two standard benchmarks Prostate (Adams

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"Why Is There a Tumor?": Tell Me the Reason, Show Me the Evidence
et al., 2022) and MSD-Prostate (Antonelli al., 2022)), to assess the model's segmentation and detection perfor- mance, zero-shot generalization capability, and rationale generation. Rationale Dataset comprises 1,500 mp-MRI seans with mask annotations at the prostate gland, zone, and lesion levels, sourced from the publicly available PI- CAI dataset (Saha et al., 2024). We enrich the image data with PI-RADS reports and rationale descriptions that cap- ture radiologists' clinical judgment, leading to 180k image- mask-rationale. We split the dataset into training and test- ing sets using an 80:20 ratio. Prostate 58 (Adams et al., 2022) includes 158 mp-MRIs with T2W, DWI and ADC se- quences. MSD-Prostate (Antonelli et al., 2022) includes 48 mp-MRIs with T2W, ADC, and DWI sequences. The task is to segment the peripheral and the transitional zones. We con- ducted training only on rationale dataset and directly evalu- ated the model on Prostate 158 and MSD-Prostate. Detailed descriptions of the datasets are available in Appendix B.
Human Evaluation of Rationale Dataset Quality
Coherence
Consistency
Comprehensiveness
Factual Accuracy
20
40
80
100
Percentage (%)
Strongly Agree
Neutral Disagree
Strongly Disagree
Agree
Figure 4: Human evaluation fo rationale quality. Expert radiologists evaluated the textual rationales across four crite- ria: coherence, consistency, comprehensiveness and factual accuracy. Each criterion was rated on a scale ranging from Strongly Agree to Strongly Disagree. The rationales re- ceived high ratings across all criteria, with strong scores in factual accuracy, highlighting the overall good quality of the rationale dataset. Each expert's rating and the experiment settings are available in Appendix A.
Baselines. We compare with U-Net (Ronneberger et al., 2015), the most referenced baselines in the literature. Addi- tionally, we include comparisons with ITUNeet (Kan al., 2022), Swin UNETR (Hatamizadeh et al., 2021), CSwin U-Net (Li al., 2023b), and SSL CSwin U-Net (Li al., 2023b), which are popular 2D and 3D models for prostate cancer segmentation and detection. Finally, we evaluate against foundation models, including SAM-based models (MA-SAM (Chen et al., 2024) and SAMed (Zhang & Liu, 2023)) and large vision-language models such as Biomed- Parse (Zhao et al., 2024).
years of professional experience, reviewed our extracted textual rationales in terms of ① coherence, @ consistency, @ comprehensiveness, and @ factual accuracy. The results in Figure 4 indicate that our rationale data are coherent, consistent, comprehensive, and highly factual accurate.
@ Coherence refers to rationales that present information in a sell-structured, logically smooth manner, ensuring that each sentence contributes to clear and unified description of tumor segmentation. Figure shows that more than 70% rationales were coherent. @ Consistency measures whether the rationale is free from self-contradictions and accurately aligns the lesion's 's characteristics with the appropriate PI- RADS category. As shown in Figure 4, over 70% of the rationales were rated as consistent, with more than 40% rating as "Strongly Agree". 3 Comprehensiveness evalu- ates rationale including imaging features such as lesion size, location, and MRI findings (e.g., non-cireumseribed, mod- erate hypointensity) and explains how they contribute to the segmentation decision. Figure 4 shows that less than 6% of the rationales were rated as not comprehensive. Noted that the "strongly agree" percentage for comprehensive- ness lower than other metrics. This is primarily due to its stricter requirement of mentioning ALL critical details. However, when combining the "strongly agree" and "agree" categories, comprehensiveness achieves a higher overall percentage than other metrics. This indicates that while some rationales might miss minor details, most success- fully include nearly all relevant imaging features. @ Factual Accuracy assesses whether the rationale provides correct information aligned with established clinical standards, e.g.
Evaluation metrics. We use the Dice Similarity Coefficient (DSC) evaluate the segmentation performance. Following the common practice for tumor detection (Saha et al., 2024), we use Area Under the Receiver Operating Characteristic (AUROC) for patient-level detection and Average Precision (AP) for lesion-level detection.
Implementation details. Our model consists of an image encoder, text encoder, and mask decoder (see Fig.3). The image encoder and mask decoder use pre-trained weights from MedSAM (Ma et al., 2024a), while the text encoder uses weights from BiomedCLII (Zhang et al., 2024). For training, we use Focal Loss (Lin, 2017) with a = 0.97 and 7 = 2, and optimize the model using the Adam (Kingma, 2014) with weight decay of 0.001 and an exponential learn- ing rate scheduler. Additional implementation details data pre-processing, post-processing, and hyperparameters are provided in Appendix D.
5.2. Human Evaluation of Rationale Dataset Quality
Takeaway: It is possible to effectively and automatically extract high-quality rationales from clinical reports.
We present the human evaluation of rationale dataset qual- ity. Two expert radiologists from MSK, each with over 10
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"Why Is There Tumor?": Tell Me the Reason, Show Me the Evidence
Table 1: Segmentation (DSC) and cancer detection (AP, AUROC) on the rationale dataset. The reported results are obtained
from our implementation and averaged over three independent runs, with 'ndicating numbers from the original paper. The best scores are highlighted bold, Our model oupperforms existing baselines lesion segmentation and cancer detection.
Method
Gland DSC (t)
Zone DSC (1)
Lesion DSC (1)
Average
AP (*)
AUROC (t)
Average
U-Net
0,882=0.00
0.880=0.007
0.504=0.011
0.755
0.518=0.055
0.855#0.028
0,687
ITUNet
0.869 #0.012
0.858=0.009
0.505+0.006
0.744
0.407=0.033
0.779#0.012
0.593
Swin UNETR
0.898=0.0220
0.860=0.004
0.431
0.737
0.513:0.044
0.852+0.020
0,682
CSwin U-Net!
0.543=0.042
0.880=0.013
0.712
SSL CSwin U-Nett
0.545#0.060
0.888+0.010
0.717
MA-SAM
0.888=0.015
0.813=0.010
0.326+0.018
0.676
0.413=0.058
0.802+0.022
0.608
SAMed
0.807#0.008
00.066
0.408+0.015
0.679
0.384x0.073
0.807+0.032
0.596
Ours
0.843=0.005
0.880=0.007
0.55220.011
0.758
0.574:0.039
0.888=0.009
0.731
Table 2: Zero-shot DSC, AP, AUROC on Prostate 158 and MSD-prostate. The model, trained on the rationale dataset, is evaluated directly on the two new datasets. Prostate 158 collected in Germany hospital presents notable distribution shifts the rationale dataset due differences in demographics or device configurations. The reported results are obtained from implementation, with 'ndicating numbers from the original paper. The best performances are highlighted in bold and A represents the improvement over the second-best model. Our model consistently outperforms existing ones in segmenting anatomical structures, delineating lesions, and detecting cancers, demonstrating strong generalization capability.
Prostate 158 158
MSD-Proshe
Method
Zone DSC (f)
Lesion DSC (t)
Average
AP (1)
AUROC (t)
Average
Zone DSC (f)
U-Net
0.767
0.348
0.558
0.491
0.784
0.638
0.709
ITUNet
0.715
0.383
0.549
0.441
0.760
0.600
0.689
Swin UNETR
0.724
0.362
0.543
0.357
0.731
0.544
0.535
CSwin U-Net!
0.363
0.772
0.568
SSL CSwin U-Nett
0.451
0.790
0.621
MA-SAM
0.709
0.220
0.465
0.392
0.816
0.604
0.690
SAMed
0.680
0.382
0.531
0.401
0.756
0.579
0.684
BiomedParse
0.356
0.187
0.271
0.002
0.423
0.213
0.461
Ours
0.787
0.426
0.607
0.565
0.835
0.700
0.717
A
+0.020
+0.043
+0.049
+0.074
+0.019
+0.062
+0.008
PI-RADS guidelines. Figure 4 results indicate that fewer than 2% rationales are considered highly inaccurate. Over 75% of the rationales were rated as accurate, with more than 43% receiving a "Strongly Agree" rating.
sion segmentation. Additionally, it outperforms the best bascline by 2.0% in cancer detection. Table 2 further high- lights the model's robust zero-shot performance, demon- strating consistent superiority on new and unseen datasets. On Prostate 158, our model surpasses the best- best-performing baselines by 8.8% in segmentation and 9.7% in cancer de- tection. Similarly, in the MSD-Prostate segmentation task, model outperforms the second best approach by 1.1%, underscoring its exceptional generalization capabilities. We present qualitative segmentation masks in Figure 5. Our method delivers high-quality segmentation results even un- der distribution shifts, demonstrating strong potential for real-worke clinical applications.
5.3. Evaluation of Model Prediction
Takeaway: Learning with additional textual rationales im- proves models' prediction accuracy and generalization. We evaluated the segmentation and cancer detection perfor- mance across multiple datasets, with results summarized in Tables 1 and 2. We observed that the additional textual rationale data helps to build models that perform better in segmenting anatomical structures and lesions, as well as detecting cancers. From Table 1, our method demonstrates a 12% improvement over SAM-based models and a 1.2% improvement over the classic model in anatomical and le-
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"Why Is There Tumor?": Tell Me the Reason, Show Me the Evidence
U-Net
MA-SAM
Ours
Ground Truth
U-Net
MA-SAM
Ours
Ground Truth
0.030
D-194
0.763
0:000
0:9
0:824
Lesion
Lesion
D0898
0.772
0.892
0:683
0.647
0:715
Zone
Zone
0.970
D.915
0.970
Gland
U-Net
MA-SAM
Ours
Ground Truth
0.895
0.843
0.939
Zone
Rationale Dataset
Prostate 158 (zero-shot)
MSD-Prostate (zero-shot)
Figure 5: Visualization of segmentation results. All models trained our Rationale Dataset's training set and evaluated on its testing set. We further evaluate the zero-shot performance of the models using Prostate 1558 and MSDD-protaat datasets, which are unseen during training. Notably, MRIs in Prostate 158 differ significantly from MRIs in training distribution. Despite this, our model achieves superior performance across all datasets, demonstrating strong generalization capability.
Table 3: Rationale accuracy on the rationale dataset. We report results for slice- and patient-level tumor classification using the model's rationale. Higher values indicate better performance. We compare with baselines using the model's logits or masks for tumor classification.
Table 4: Visual evidence accuracy on rationale dataset, Prostate 158, and MSD-Prostate. The evaluation measures how accurately the model localizes rationales in the im- ages. Results are compared with a variation our model, which optimized without the disentangle and localization constraint (w/o constraint). Our model demonstrates sig- nificant improvement in rationale localization.
Slice-level
Patient-level
Method
Precision
Recall
F1
Precision
Recall
F1
Method
Rationale Dataset
Prostate 158
MSD-Prostate
Logits
0.563
0.305
0.396
0.508
0.426
0.464
Mask
0.236
0.874
0.927
Visual evidence (RMA f)
Ours
0.633
0.513
0.567
0.857
0.412
0.583
w/o constraint
0.010
0,024
0.012
Ours
0.403
0.330
0.497
Segmentation (DSC 1)
5.4. Evaluation of Model Rationale
w/o constraint
0.758
0.607
0.717
Ours
0.746
0.589
0.687
Takeaway: is feasible to build rationale model without human annotation at the pixel level.
Accuracy (RMA) (Arras et al., 2022). RMA measures the ratio of the sum of heatmap values within the ground truth mask to the total heatmap values, defined as:
We evaluate the accuracy of model's prediction ratio- nales and its ability localize the rationales within the images. For comparison, we also tested OpenAl's ol model for rationale generation and localization.
RMA(H, M) EiiyYEROR H.j Maj
(5)
Rationale and visual evidence. Our model, during infer- ence, generates rationales for given image by performing image-to-text retrieval from a pool of concepts that deseribe the tumors in the learned vision-language embedding space. For each retrieved rationale, heatmaps localizing the most relevant pixels are generated by computing the similarity between patch-level embeddings and textual rationale em- beddings. Rationale accuracy is quantified by verifying whether the generated rationales are tumor-related and vi- sual evidence accuracy is measured using Relevant Mass
E(so) Hig
where H is the heatmap, is the binary ground truth mask, and ROI represents the spatial indices where My 1. Results in Tables 3 and 4 demonstrate that model signif- icantly outperforms the baseline in both rationale accuracy and localization.
Comparison with OpenAl's ol. Existing MLLMs, such as OpenAl's ol, are also capable of generating textual justifica- tions for their predictions. To evaluate rationale generation,

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"Why Is There a Tumor?": Tell Me the Reason, Show Me the Evidence
OpenAl o1 (Pure Text)
Ours (Concepts & Visual Evidence)
Quantitative Results
The identified lesion appeaning as nonicincumn www.ciccummssccc mnscrbee dlldeffied area with focal low signal on 12-weighted mmeging and reetricted diffusion (bright on high b-velue DWI, on ADC). Meesuring lpprokimaley 1.0 om, does
pampheral
Lesion #1
Random Guess
OpenAi 01
Ours
0.50
0.88
Lesion Transition Zone
0.50
0.46
0.50
exceed 1.5 6m maximal cimension No cloar
T2W shows confined to prostate. moderatl hypointense.
0.2
0.22
signs extracapsular extensionn seminal vesicle ivvasion or adjasent organ invadion are observad on these limited views. Besed on these cheracteristics, lesion is assigned a BRADS scgre
Tumar Classification
Tumor Location
PI-RADS Score
PI-RADS Score 5.
Figure 6: Comparison between OpenAl ol and our model. Qualitatively, ol provides only textual Chain-of-Thoughhe it might be wrong about critical concepts (highlighted in rad). In contrast, our model offers prediction rationales using clinical concepts supported by valid visual evidence. Quantitatively, ol performs worse than random guessing in Tumor Classification, Location, and PI-RADS Score prediction, whereas our model achieves significantly higher accuracy.
Table 5: Ablation training without any constraints (w/o constraint), with disentangle constraint (w/ disen.), and with both constraints (ours) on the rationale dataset.
6. Related Work
Medical image segmentation. Recent advances in medical image segmentation, such as nnU-Net (Isensee et al., 2021). UNETR (Hatamizadeh et al., 2022). Swin- UNETR (Hatamizadeh et al., 2021), and MedSAM (Ma et al., 2024a), have achieved state-of-the-an performance in lesion and organ segmentation. However, most of these models produce only segmentation masks labels, offering no insight into why a region is clinically significant. In contrast, our framework not only generates precise segmen- tation masks but also justifies predictions with clinical terms and points to anatomically localized evidence
discntangle
localize
Segmentation
Visual Evidence
w/o consraint w/ disen.
0,758
0.010
0,747
0.024
Ours
0.746
0.403
w/o constraint
w/ disen.
Ours
"homogenous moderate hypointense"
Medical image analysis beyond accuracy. Prior efforts to enhance medical AI interpretability fall into three cate- gories: First, concept-based models (e.g.. CBMs (Koh et al., 2020), ProtoPNet (Chen et al., 2019)) learn the mapping from latent representation to high-level concepts but fail to localize them anatomically (Margeloiu et al., 2021). Sec- ond, explainable methods (e.g., GradCAM (Selvaraju et al., 2020; Li al., 2023a)) highlight regions influencing pre- dictions but omit semantic links to clinical terms. Third, vision-language models (e.g., BiomedParse (Zhao al., 2024), multimodal models (Zhang et al., 2024; Ma et al., 2021; Wang et al., 2022; Ma et al., 2022)) align image-text pairs globally but struggle to disentangle fine-grained con- cepts (Li et al., 2024c;b). Multimodal large language mod- els (e.g.. LLa (Li et al., 2024a)) trained on clinical content are capable of generating textual rationales but lack spatial grounding or segmentation capabilities. Additionally, while existing datasets (e.g., MIMIC-CXR (Johnson et al., 2019)) provide images and clinical reports, they often lack detailed descriptions of rationales. There are Al models try to provide language justification for their and grounded each sentence to the input image with a bounding box (Bannur et al., 2024; Fallahpour et al., 2025). However, these ap- proaches typically require ground truth bounding boxes for each sentence. Differently, we introduce rationale dataset, containing paired images, masks, and mechine-readable
"lenticular"
Figure 7: Visualization of visual evidence. Using the disen-
tanglement constraint alone will lead to trival solutions.
we tested ol on 20 selected MRI scans, containing posi- tive and 11 negative MRIs. Figure 6 presents comparison of the generated rationales. Our model demonstrates su- perior performance, providing accurate rationales that are consistently supported by valid visual evidence. In contrast, ol shows lower rationale accuracy and cannot localize the rationales in the image.
5.5. Ablation on the Constraints
Effects of the constraint. We conduct ablation on the contribution of different constraints in terms of segmenta- tion accuracy and rationale localization. Quantitative and qualitative results are provided in Table 5 and Figure 7, respectively. The results shows that both disentangle and localization are indispensable for accurate concept local- ization. Additionally, we provide analysis of our model's failure case in Appendix E.
8

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"Why Is There a Tumor?": Tell Me the Reason, Show Me the Evidence
rationales, and self-supervised optimization to jointly seg- ment lesions, justify predictions with clinical terms, and localize their visual evidence.
model's explanations mimic clinical reasoning, they are substitute for radiologist judgment; over-reliance Al could lead to diagnostic complacency. We emphasize that our tool is an assistive, not autonomous, decision-maker. Second, patient privacy is safeguarded by using only de- identified, publicly available data.
7. Conclusion, Limitations, and Future Work
Conclusion. We propose to develop models that justify predictions using clinical terms and localize their visual evi- dence, bridging the semantic-visull gap that hinders clinical trust. Two key challenges addressed: First, tackle the lack of prediction and rationale pairs for model training by curating the first rationale dataset pairing 1.5K prostate MRI scans with 180K detailed language rationales. Second, we introduce rationale-informed optimization to disentangle and localize the clinical concepts without pixel-level anno- tations. Empirical evaluations show that our model delivers accurate rationales, each supported by valid visual evidence.
References
Achiam, J., Adler, S., Agarwal, S., Ahmad, L.,, Akkaya, I., Aleman, L.,, Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023.
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Limitations and future work. While our current rationale dataset focuses exclusively on prostate cancer, we have developed a scalable pipeline for generating detailed, high- quality textual rationales from structured clinical reports. This foundation enables future expansion to other cancers, such breast, liver, and lung, where rich sources of clinical reports are available.
Antonelli, M., Reinke, A., Bakas, S., Farahani, K., Kopp- Schneider, A., Landman, B. A., Litjens, G., Menze, B., Ronneberger, O., Summers, R. M., et al. The Medical Segmentation Decathlon. Nature communications, 13(I):
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Acknowledgment
Arras, L., Osman, A., and Samek, W. Clevr-xai: A bench- mark dataset for the ground truth evaluation of neural network explanations. Information Fusion, 81:14-40, 2022.
This work is supported by the National Science Foundation under grant numbers CAREER 2340074, SLES 2416937, III CORE 2412675 and National Institutes of Health under grant number R21CA301093, Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not reflect the views of the supporting entities.
Bannur, S., Bouzid, K., Castro, D. C., Schwaighofer, A., Thieme, A., Bond-Taylor, S., Ilse, M., Pérez- Gareía, F., Salvatelli, V., Sharma, H., et al. Maira-2: Grounded radiology report generation. arXiv preprint arXi::2406.04449, 2024.
Impact Statement
Our work addresses a critical barrier the clinical adoption of medical AI: the lack of interpretable, auditable reason- ing in model predictions. By enabling models to justify segmentation and detection with clinical terms and localize their evidence, we enhance trust and safety in Al-assisted diagnosis. This is particularly impactful in radiology, where misinterpretatiios can lead to delayed treatment or unnec- essary biopsies. Our rationale dataset and self-supervised framework set standard for interpretable medical AI, empowering radiologists to audit model logic and acceler- ating translational research. By aligning Al outputs with clinical workflows, our method could potentially reduce diagnostic errors and radiologist workloads, and improve patient outcomes in resource-constrainee settings.
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"Why Is There Tumor?": Tell Me the Reason, Show Me the Evidence
A. Detailed Results of Human Evaluation on Rational Dataset Quality
Consistency
Factuall Acnuracy
Exper
46 5%
27. 27.3%
10.1%
17 2%
Expert
44 4%
31.3%
6.1%
2%
2 200
Exper11
43 0%
27.7%
10 0%
16
Expert
6%
8%
6.4%
17 0%
40
8b
28
Ratings
Percentage Ratlins
Strongll Agrms
Agren
Neutril
sagree
Strongly Daggre
Strong'y y Agren
Agren
Nwutril
Disagree
Strong Disagnee
Comprehenssivnness
Coherence
2
Expart
36
16 0%
67.0%
11.7% 8%
Expert
36.1%
38.0%
10.8%
21.3%
Percentage Relings (%) Nextral
Percenlage Ratings (%) Nextrall
Strongly
Figure 8: Human evaluation of rationale quality. We randomly sample 100 the extracted rationale descriptions for evaluation. Each radiologist reviewed all 100 cases.
B. Detailed Dataset Description
B.1. PI-CAI Challenge
Rationale dataset is derived from the Public Training and Development Dataset of the PI-CAI challenge (Saha et al., 2024), which comprises 1,500 anonymized prostate mpMRI scans from 1,476 patients. These scans were collected between 2012 and 2021 across three centers in the Netherlands: Radboud University Medical Center, University Medical Center Groningen, and Ziekenhuis Groep Twente. Each patient case was annotated based on histologicalll with Gleason grade group 2 2 classified as positive and Gleason grade group < or PI-RADS classified as negative. For all cases, clinically significant prostate cancer (csPCa) lesions were delineated by one of ten trained investigators or radiology resident.
findings,
Of the 1,500 cases, 1,075 cases contain benign tissue or indolent prostate cancer (PCa), while 425 cases contain csPCa. Among these 425 positive cases, 220 have expert-derived lesion annotations, while the remaining 205 have Al generated lesion annotations. All 1,500 cases have Al generated Gland, Peripheral Zone and Transition Zone annotations. Following the setup provided by the challenge organizers, we combined the human labeled data and AI labeled data and further split them (1,500 cases) into five folds. Then we train and validate all the models with the five folds generated.
B.2. Prostate158
Prostate 158 (Adams et al., 2022) includes 158 expert-annotated 3T prostate MRIs with T2-weighted (T2w) and diffusion- weighted imaging (DWT) sequences, including ADC maps, similar to those in PI-CAI. These MRIs were collected at Charité University Hospital in Berlin, Germany, from February 2016 January 2020. Images were acquired on Siemens VIDA and Skyra 3T scanners (Siemens Healthineers, Erlangen, Germany) following protocols aligned with current guidelines and using B1 shimming.
Two board-certified radiologists, with and 8 years of experience in uro-oncologic imaging, annotated all MR images. They provided pixel-wise segmentations for the central gland (central and transitional zones), peripheral zone, and prostate cancer (PCa) lesions, defined as suspicious areas with a PI-RADS score t= 4. All PCa lesions were segmented on the ADC map and correlated with T2w sequences and high b-value DWT images. We collected anatomical segmentation label from reader
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1 on T2w sequences and tumor segmentation label from reader on the ADC map
B.3. MSD-Prostate
MSD-Prosttte (Antonelli al., 2022). This is the prostate subset of Medical Segmentation Decathlon (MSD) dataset. This subset consists of 48 prostate mp-MRI studies comprising T2-weighted and apparent diffusion coefficient (ADC) series, which acquired Radboud University Medical Center, Nijmegen Medical Center, Nijmegen, The Netherlands. The data set was further divided into training set and testing set. Pixel-wise segmentation masks of the prostate peripheral zone (PZ) and the transition zone (TZ) were provided for the training set. We only use the training set for our zero-shot evaluation.
C. Our Prompts
C.1. The full prompt for rationale generation
Prompt to Extract Rationale From PI-RADS Report
Here is the PI-RADS decision tree that summarizes radiologists' criteria to assign the PI-RADS for prostate cancer
lmmors:
PI-RADS Decision Tree:
1. If "Lesion Local" is in the "Peripheral zone":
Check ADC/DWI MRT:
If "Uniform hyperintense signal intensity (normal)" DWI score PI-RADS score
If "Linear or wedge-shaped hypointensity or diffuse mild hypointensity, usually indistinct margin" - DWI
score 2 + PI-RADS score
If "Heterggenooos signal intensity or non-circumscribed, rounded, moderate hypointensity"" - DWI score
=3 =3:
Check DCE MRI:
If DCE is "positive" + PI-RADS score = 4
If DCE is "negative" - PI-RADS score =
If "Circumseribed, homogenous moderate hypointense focus/mass confined the prostate and 1.5 in greatest dimension" + DWI score 4 + PI-RADS score = 4
If "Circumscribed, homogenous moderate hypointense focus/mass confined to prostate and > 1.5 cm in greatest dimension, definite extraprostatic extensionninvasive behavior" DWI score 5 PI-RADS
score 5
If LLesion Local" the "Transition zone":
Check T2W MRI:
If "Normal appearing T7. (rare) or round, completely encapsulated nodule" - T2W score = PI-RADS
score =
If "A mostly encapsulated nodule OR a homogeneous circumscribed nodule without encapsulation" +
T2W score = 2:
Check DWI score:
If DWI score 4 + PI-RADS score = 2
If DWI score 4 PI-RADS score =
If "Heteroggnneoous signal intensity with obscured margins" T2W score 3:
Check DWI score:
If DWI score 5 PI-RADS score = 3
If DWI score < PI-RADS score =
If "Lenticular non-cireumscribed, homogencous, moderately hypointense, and 1.5 em in greatest dimension" + T2W score 4 PI-RADS score =
14

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"Why Is There Tumor?": Tell Me the Reason, Show Me the Evidence
If "Lenticular or non-cireumscrrhed, homogeneous, moderately hypointense, and 1.5 em in greatest dimension or definite extraprostatic extension/invasive behavior" T2W score - PI-RADS score
Examples of Radiologist Justifications:
Example 1: PI-RADS Report for Patient 10418
PI-RADS Score: 4
Size: 1.4 x 1.1 cm
Location: Right, anterior, midgland, transition zone
Example 1 Justification: The lesion is located the transition zone, specifically in the right anterior midgland, with size of 1.4 1.1 em. The MRI shows lenticular, non-cireumscribed, homogeneous, moderately hypointense lesion, and its size is less than 1.5 cm in the greatest dimension. These characteristics correspond to T2W score of 4. Since there are no signs of extracapsular extension, seminal vesicle invasion, or adjacent organ invasion, the lesion's ' PI-RADS score is 4.
Example PI-RADS Report for Patient: 10424:
PI-RADS Score: 5
Size: 2.6 x 1.4 cm
Location: Bilateral, posterior, base apex, peripheral zone
The lesion is located the peripheral zone, extending bilaterally from the base to the apex. On the MRI, measures 2.6 em. The lesion shows cireumscribed, homogenous moderate hypointense focus/mass confined the prostate, greater than 1.5 em in the greatest dimension, which corresponds DWI score of 5. Additionally, there is evidence of extracapsular extension and adjacent organ invasion. Given these characteristics and the lesion's 's extent, the lesion's 's overall P1-RADS score is 5.
Example 2 Justification:
Task: Could you write a rationale justification for the following patient based on the patient's PI-RADS report and PI-RADS decision tree? Your justification should be short, clear, and follow the logic of the PI-RADS decision tree.
Here goes the new PI-RADS report.
PI-RADS Score: 4
Size: 1.4 1.1 cm
Location: Right, anterior, midgland, transition zone
C.2. The full prompt for evaluation with OpenAT
Prompt to Evaluation with OpenAl o1
These are T2, DAC, and DWI MRIs for prostate cancer. First, could you tell me the Image contains tumors? there is a tumor, where is the location of the tumor? (Peripheral zone or transitional zone?) Then, the lesion margin non-cireumscrieed cireumseribed? Does the lesion's greatest dimension is greater than 1.5 cm? Does the lesion show signs of extracapsular extension, seminal vesicle invasion, adjacent organ invasion? What's 's your judgment of the PI-RADS category? Please give me a definitive answer.
D. Additional Implementation Details
Preprocessing, We followed the same preprocessing procedure with the PI-CAI challenge in all three datasets. First, the mpMRI sequence are resampled to a spacing (3mm, 0.5mm, 0.5mm), then center crop the sequence to (24, 384, 384). To meet the input requirement of our model, all images are bilinearly resized to (24, 1024, 1024), then, z-norm is applied to each slice in the sequence.
15

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"Why Is There Tumor?": Tell Me the Reason, Show Me the Evidence
Training. To tackle the severe long-tail condition, we sample slices with a significant lesion (spanning 2 0.001 of the image) and nonsignificant lesion (< 0.001) with probability of 0.5. We use Focal loss with 0.97 and y 2. We Adam optimizer with Betas [0.9,0.999], weight decay 0,001 and exponential learning rate schedule with initial Ir 0.0001 and exponent = 0.9. For online image augmentation, we apply random ratation by choosing the rotation angle between [-15, -00, -5, 0, 5, 10, 15] degrees, and randomly flip horizontally and vertically.
For all baseline and our models, we perform 5 fold cross-validation and
Postprocessing, We applied erosion and dilation the generated lesion mask eliminate small bubbles. And according to the predicted gland mask, automatically delete those lesions predicted that have 0.5 overlap with gland mask. The overlap is defined as:
Mg Mr [M
overlop
Where M, and Mj denotes predicted gland mask and lesion mask.
Evaluation metrics. We use Dice score (DSC) for evaluating the segmentation accuracy across all the output channels (Gland, Zone and Lesion). Dice score is defined
2|AAn + [A| + / +
Dice
Where A,B denotes the predicted mask and ground truth mask respectively. is small number that preventing dividing by O. We choose = 1. Considering there is significant amount of images which don't have '1's the lesion ground truth, making very easy for the model to get a very high lesion dice score (+s 0.93 by only output empty mask for lesion), we only report the mean dice score of lesion positive images, meaning images having 'I's in their lesion mask. For detection metrics, we adopt the same metrics from the PI-CAI challenge (Saha et al., 2024). The performance of patient-level diagnosis is assessed using the Area Under the Receiver Operating Characteristic (AUROC) metric, while lesion-level detection performance is measured with the Average Precision (AP) metric.
E. Analysis of Failure Case
Figure visualizes the failure cases of our model, highlighting an inconsistency between segmentation masks and the corresponding rationales. While the model provides both segmentation masks and the reasoning behind decisions, we observe that these outputs could be inconsistent, particularly in diagnostically transition slices where the tumor just starting to appear or fade. There two types of inconsistency. Case 1: mask without rationale. The model predicts tumor segmentation while generating benign rationales. Case 2: rationale without mask. The model output tumor rationales while failing to produce the tumor segmentation. Both cases frequently occurs in transitional slices, where tumor boundaries are ambiguous or tumor presence is subtle. This rationale-mast misalignment could undermine clinical trust of our Al models. will explore methods to resolve this misalignment in the future work.
Slice
MRI Sequence 1
Slice i+j
Slice m
MRI Sequence 2
Slice m
GT
Mask
Pred Mask
Any tumor
"No"
"Yes"
"Yes"
"Yes"
"No"
"No"
"Yes"
"Yes"
"No"
"No
rationale?
Figure 9: Visualization of failure cases (the inconsistency between segmentation mask and rationale).
16

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"Why Is There Tumor?": Tell Me the Reason, Show Me the Evidence
F. Future Direction
Expanding the rationale dataset. Although our dataset currently supports robust localized explanations for prostate cancer, natural next step involves extending this dataset include other cancer types, such as breast (Cozzi et al., 2024) and lung (Mehta al., 2017) cancers, where interpretable is equally critical. In addition expanding data collection within single institution, future efforts could utilize distributed learning techniques (McMahan et al., 2017; Ma et al., 2024b; Li et al., 2020) to collaboratively train models across multiple hospitals without sharing raw data, implicitly enlarging the effective training dataset.
Building an education tool for junior or attending radiologists. Diagmostic disagreements between expert and junior radiologists- caaioooggsssppprtclllaaa in nuanced cases like PI-RADS scoring-hhighighh a critical training gap. It is possible to leverage our model's rationale generation ability to build an AI-driven education tool. This tool would enable trainees to analyze cases, submit assessments, and receive feedback contrasting their decisions with expert-backed AI rationales.
17


Reviews:
[{"review_id": "c8qxt4YgGa", "reviewer": "Reviewer_M2zk", "summary": "This paper addresses the challenge of enhancing interpretability in medical AI models for tumor detection and segmentation. The authors propose a novel framework that generates predictions supported by both clinical concepts and visual evidence. To achieve this, they curate a “first-of-its-kind” dataset (will make the dataset publicly available.) containing 180K image-mask-rationale triples, where rationales are high-quality textual justifications for clinical assessments. Additionally, they introduce a rationale-informed optimization method that disentangles and localizes fine-grained clinical concepts without requiring pixel-level annotations. Experiments on multiple medical benchmarks demonstrate superior performance in segmentation, detection, and rationale correctness compared to state-of-the-art models.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate your encouraging feedback. The following are our point-to-point responses to the comments.\n\n> **Q1:** Verification on other types of tumors.\n\n**A1:** We fully agree that our method will be further strengthened with validation on additional tumor types (e.g., breast, lung, liver). **However, other tumors often do not have rationale datasets.** Although our paper provides a detailed pipeline to curate the rationale dataset, completing the entire curation process is non-trivial and cannot be finished in the short term. In this pilot study, we use prostate cancer to prove the concept of our method. Due to character limits, we provide a detailed discussion on why rationale data curation is non-trivial, why prostate cancer, and how our method genrealize to other tumors in response to Rviewer W914. Please refer to that response for the full context.\n\n> **Q2:** Is our PDT (PI-RADS Decision Tree) based on clinical standards?\n\n**A2:** Yes, the PDT was derived directly from the latest PI-RADS v2.1 guideline, which is the widely adopted clinical standard for prostate MRI interpretation.\n\n> Q3: Notation definition, a more detailed Loss, and clear explanation of Equations (3) and (4).\n\n**A3:** $c_k$ and $c_k’$ denote distinct clinical concepts (e.g., $c_k$ = “heterogeneous signal intensity”; $c_k’$ = “non-circumscribed margin”). $\\epsilon_1$ and $\\epsilon_2$ are thresholding parameters that control the localization of the clinical concepts. Intuitively, **Equation 3** penalizes overlap between highlighted pixels (heatmaps) of clinically distinct concepts. **Equation 4** enforces concepts describing the same structure to activate within its anatomical boundaries. \n\nWe use the KKT condition and Lagrange multipliers to convert Equation 4 into unconstrained optimization. This leads to a loss function $\\mathcal{L}$ that combines the main objective (segmentation + contrastive learning) $\\mathcal{L}_{main} $ with the disentanglement $\\mathcal{L}_d$ and localization $\\mathcal{L}_c$ constraints. Contributions of the two constraints are balanced by Lagrange multipliers $\\lambda$ and $\\gamma$. The final loss is as follows:\n\n$\\mathcal{L} = \\mathcal{L}_{main} + \\lambda \\mathcal{L}_d + \\gamma \\mathcal{L}_c$\n\n> **Q4:** Providing examples to explain the localization constraint.\n\n**A4:** For example, \"peripheral zone\" (anatomical region) and \"non-circumscribed margin\" (feature of a lesion) should localize within the prostate gland, not unrelated regions like the bladder (outside the prostate). \n\n> **Q5:** An analysis of failure cases would be meaningful in assisting doctors to assess the model's predictions.\n\n**A5:** Thanks for this critical point. It exactly highlights the benefits of providing rationales for model prediction. **Rationales offer a new lens for doctors to access the prediction.** For instance, if the model fails to highlight a tumor in an image (no mask) but still mentions cancer-related concepts (like “obscured margins”), this mismatch warns doctors that the prediction might be wrong. \n\nOur results in Table 3 indicate that the **prediction and rationales are not always consistent.** The task in Table 3 is to classify MRI scans as “tumor” or “not tumor”. We compared two methods. Method 1 uses the model’s segmentation mask (if the model draws a mask = tumor). Method 2 uses the model’s rationale (if cancer-related concepts are activated = tumor). Results showed rationales worked better, especially at the slice level, indicating that predictions and rationales don’t always align. \n\n**Failure case analysis.** Tumors are often difficult to detect in transitional slices—those where the tumor is just starting to appear or fade—leading to potentially incorrect segmentations (e.g., missing a tumor or flagging a healthy area). However, the rationales could still highlight some tumor-related concepts. This inconsistency raises alarm to the doctors that the model potentially makes a wrong segmentation. We will include the failure case analysis in the final version. \n\n> **Q6:** Validating the proposed approach on different backbones.\n\n**A6: Why do we focus on MedSAM?** We adopted MedSAM due to its state-of-the-art performance and recent success in building medical foundation models (e.g., BiomedParse). MedSAM is pre-trained on multiple modalities (MRI, CT, X-Ray, etc). This lays out a solid foundation when scaled up to other tumors. **How about using other backbones?** In response to the suggestion, we conducted experiments using TransUNet as the backbone. Cancer detection results are reported in the following Table 6. **Our method improves TranUNet’s baseline performance by 6.2%, demonstrating its generalizability to other backbones.**\n\nTable 6. Cancer detection (AP and AUROC) with TransUNet as the backbone on the rationale dataset.\n| | AP | AUROC | Average |\n|---|:---:|:---:|:---:|\n| Baseline | 0.416 | 0.811 | 0.614 |\n| Ours | **0.463** | **0.840** | **0.652** |", "claims_and_evidence": "The authors' experiments (both internal and external dataset) can support their claims. However, one weakness is that while the authors' method is claimed to be generalizable across different types of tumors, it has only been validated on prostate cancer, lacking verification on other types of tumors.", "methods_and_evaluation_criteria": "Is the formulation of PDT (PI-RADS Decision Tree) based on clinical standards, rather than merely “developed with radiologist alignment”. That is, whether there are clinical standards as a basis rather than the radiologist 's subjective opinion.", "theoretical_claims": "The proofs in the paper are validated through experiments rather than relying on theoretical demonstrations. However, the explanations of the optimization formulas should be made clearer.  Specifically, the meanings of \\( c^{'}_{k} \\) and ϵ1 and ϵ2 are not clearly defined in Equations (3) and (4). Additionally, a more detailed Loss and clear explanation of the formulas (3) and (4) should be provided.\"\nIn the part of Localization constraint, provide examples to explain what is meant “Our idea is that different concepts describing the same anatomical structure”", "experimental_designs_or_analyses": "1. The authors should analyze failed cases in the segmentation examples (including true positives and false positives) along with their corresponding explanatory rationales. It is important to investigate whether incorrect segmentations lead to errors in the rationales and to evaluate the consistency between segmentation results and rationales. Such an analysis of failure cases would be more clinically meaningful in assisting doctors to assess the model's predictions.\n2. The authors have only validated the experiments on the MedSAM backbone. As a general method, they should validate the universality of the proposed approach on different segmentation backbones (SwinUNETR, UNet, nnUNet).", "relation_to_broader_scientific_literature": "I find this to be an interesting work that addresses the limitations of traditional methods (e.g. GradCAM) in terms of medical AI interpretability. Additionally, the approach used for constructing the dataset is also noteworthy and provides valuable reference for future research.", "essential_references_not_discussed": "No", "other_strengths_and_weaknesses": "Even though the author designed Human evaluation to assess the quality of rationales, it is insufficient with only two readers. Besides the reader study, I think the author should design an automated quantitative assessment of the rationales' quality to evaluate the accuracy of the tumor diganosis.", "other_comments_or_suggestions": "If the author can conduct verification on multiple tumors and multiple backbones (such as the more mainstream nnUNet), it would be more convincing.", "questions_for_authors": "1.\tThe authors' experiments (both internal and external dataset) can support their claims. However, one weakness is that while the authors' method is claimed to be generalizable across different types of tumors, it has only been validated on prostate cancer, lacking verification on other types of tumors.\n2.\tIs the formulation of PDT (PI-RADS Decision Tree) based on clinical standards, rather than merely “developed with radiologist alignment”. That is, whether there are clinical standards as a basis rather than the radiologist 's subjective opinion.\n3.\tThe meanings of $ c^{'}_{k} $ and ϵ1 and ϵ2 are not clearly defined in Equations (3) and (4). Additionally, a more detailed Loss and clear explanation of the formulas (3) and (4) should be provided.\"\n4.\tIn the part of Localization constraint, provide examples to explain what is meant “Our idea is that different concepts describing the same anatomical structure”\n5.\t. The authors should analyze failed cases in the segmentation examples (including true positives and false positives) along with their corresponding explanatory rationales. It is important to investigate whether incorrect segmentations lead to errors in the rationales and to evaluate the consistency between segmentation results and rationales. Such an analysis of failure cases would be more clinically meaningful in assisting doctors to assess the model's predictions.\n6.\tThe authors have only validated the experiments on the MedSAM backbone. As a general method, they should validate the universality of the proposed approach on different segmentation backbones (SwinUNETR, UNet, nnUNet).", "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": {"ethical_review_concerns": "No"}}, {"review_id": "3PYaIJntOZ", "reviewer": "Reviewer_W914", "summary": "This work addresses a highly interesting topic by formulating the task of tumor localization, which involves explaining and identifying tumor regions in medical data. The authors construct a dedicated dataset for this task and establish methods to quantify both the performance and explainability of their approach.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate your encouraging feedback. The following are our responses to the comments.\n\n> **Q:** Since all experiments are conducted on Prostate MRI scans, it raises concerns about the generalizability of the proposed method. Can this framework be extended to other modalities (e.g., CT, X-ray) or different body parts? The authors should clarify why they focused solely on Prostate MRI and discuss the potential limitations or adaptations required for broader applicability.\n\n**A:** Thank you for raising this critical point. We fully agree that our method will be further strengthened with validation on additional tumor types (e.g., breast, lung, liver). **However, other tumors often do not have rationale datasets.** Although our paper provides a detailed pipeline to curate the rationale dataset, completing the entire curation process is non-trivial and cannot be finished in the short term. In this pilot study, prostate cancer serves as an impactful proof of concept for our method.\n\n**Why is curating a rationale dataset non-trivial?** Obtaining a high-quality rationale dataset requires specialized radiologists’ annotations and extensive multi-institution coordination. For example, in our study, radiologists spent over 150 hours generating rationale data (as mentioned in lines 80–81), but due to clinical duty, they couldn’t dedicate full-time effort. The entire process takes two and a half months to finish. Moreover, data-sharing and legal constraints further delay the process. \n\n**Why prostate cancer?** Prostate cancer is the second leading cause of cancer death in men. Delivering a high-performance AI model with improved interpretability already brings substantial value to clinical practices. \n\n**Why is our method applicable to other tumors? (1) From the data perspective,**  the core components to curate a rationale dataset are standard guidelines (PI-RADS) and clinical reports. In other tumor types, such guidelines (BI-RADS [1], Lung-RADS [2], and LI-RADS [3]) and reports are also available. Therefore, it is possible to represent the domain knowledge in proper format (e.g., Trees) using the guidelines and leverage automatic method to generate the rationale data;  **(2) From the model perspective,** different tumor types may use different medical imaging modalities for diagnosis such as breast (X-Ray, ultrasound, and MRI), Lung (CT), Liver (CT and MRI) cancer. Our adopted backbone model (MedSAM) is pre-trained across multiple modalities (MRI, CT, X-Ray, Ultrasound, etc), providing a solid foundation model to handle other tumors. \n\n**We will revise our claims in the paper.** In the limitations section, we briefly discuss the paper’s current focus on prostate cancer, and we will revise the text to clarify that our method holds the potential to generalize to other tumors but is currently only evaluated in prostate cancer. Additionally, we are actively collaborating with leading cancer centers to **curate rationale datasets for breast and liver cancers**, with completion expected by Fall 2025. All rationale datasets will be open-sourced to accelerate community efforts.\n\n*References:*\n\n[1] Spak et al. \"BI-RADS® fifth edition: A summary of changes.\" Diagnostic and interventional imaging 98.3 (2017): 179-190.\n\n[2] Christensen et al. \"ACR Lung-RADS v2022: assessment categories and management recommendations.\" Journal of the American College of Radiology 21.3 (2024): 473-488.\n\n[3] Chernyak et al. \"Liver Imaging Reporting and Data System (LI-RADS) version 2018: imaging of hepatocellular carcinoma in at-risk patients.\" Radiology 289.3 (2018): 816-830.", "claims_and_evidence": "yes", "methods_and_evaluation_criteria": "yes", "theoretical_claims": "this is not a theory work.", "experimental_designs_or_analyses": "yes", "relation_to_broader_scientific_literature": "The limits of fair medical imaging AI in real-world generalization. Nature Med", "essential_references_not_discussed": "No", "other_strengths_and_weaknesses": "#### **Strengths**\n1. **Novel Framework for Tumor Localization**  \n   - The authors propose a novel framework to analyze the reasoning behind tumor localization tasks, which is an innovative and underexplored area in medical imaging research.\n\n2. **Dataset Contribution**  \n   - The introduction of a new dataset specifically designed for this task is a significant contribution. If the authors release this dataset publicly, it could greatly benefit the research community and foster further advancements in the field.\n\n3. **Quantifying Explainability**  \n   - This work is the first to quantify the rationality and explainability of tumor localization, providing a systematic way to evaluate both performance and interpretability, which are critical in medical applications.\n\n#### **Weaknesses**\n1. **Generalizability to Other Modalities or Body Parts**  \n   - Since all experiments are conducted on Prostate MRI scans, it raises concerns about the generalizability of the proposed method. Can this framework be extended to other modalities (e.g., CT, X-ray) or different body parts? The authors should clarify why they focused solely on Prostate MRI and discuss the potential limitations or adaptations required for broader applicability.", "other_comments_or_suggestions": "please see the weakness part.", "questions_for_authors": "please see the weakness part.", "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": "HnPfR7Ia9J", "reviewer": "Reviewer_XDug", "summary": "Medical AI models effectively detect and segment tumors but often fail to provide explicit clinical reasoning, making their outputs less trustworthy in practice. Existing methods either localize abnormalities without justification or generate textual explanations without spatial grounding. To bridge this gap, the authors curate a dataset of 180K image-mask-rationale triples, verified by expert radiologists, and develop a self-supervised model that disentangles and localizes fine-grained clinical concepts without requiring pixel-level annotations.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate your encouraging feedback. Please find our point-by-point responses to the comments below.\n\n> **Q1:** Do the Dice loss and InfoNCE loss have the same weight in Eq. 2? \n\n**A1:** The Dice loss and InfoNCE loss are balanced using a hyperparameter rather than having equal weights. We will revise the equation accordingly.\n\n> **Q2:** Some diseases affect multiple regions or share visual features, while others are diffuse. The localization constraint may address the first case, but how do you handle the second? Can you clarify how the model adapts to conditions requiring multiple highlighted areas?\n\n**A2:** Our method optimizes for a balance between overlap and separation of clinically distinct concepts rather than enforcing strict regional exclusivity. As shown by the visualizations in Figure 3 (right panel), the model can highlight both separate and partially overlapping regions corresponding to related concepts.\n\n**How about diffused conditions?** In these scenarios, such as tumors occupying nearly the entire prostate gland, our method can still learn nuanced regions corresponding to distinct tumor-related concepts. However, for certain anatomical structures (e.g., transition zone), our approach may not correctly localize them due to visual absence.\n\n\n> **Q3:** Why is comprehensiveness scored lower than other criteria in Section 5.2?\n\n**A3:** Comprehensiveness evaluates whether the rationale includes **ALL** details needed to support the tumor segmentation.  The lower \"strongly agree\" percentage for comprehensiveness compared to other metrics is primarily due to its stricter requirement of mentioning ALL critical details. However, when combining the \"strongly agree\" and \"agree\" categories, comprehensiveness actually achieves a higher overall percentage than other metrics. This indicates that while some rationales might miss minor details, most successfully include nearly all relevant imaging features.\n\n> **Q4:** What does the second \"average\" column in Table 2 represent?\n\n**A4:** AUROC reflects patient-level diagnostic performance, while AP captures lesion-level detection performance. Following common practice [1], we report their average as a holistic measure of the model's performance.\n\n> **Q5:** Why was MedSAM chosen as the backbone instead of U-Net? Have you compared performance between the two architectures?\n\n**A5: Why do we focus on MedSAM?** We adopted MedSAM due to its state-of-the-art performance and recent success in building medical foundation models (e.g., BiomedParse). MedSAM is pre-trained on multiple modalities (MRI, CT, X-Ray, etc). This lays out a solid foundation when scaled up to other tumors. **How about using other backbones?** In response to the suggestion, we conducted experiments using TransUNet as the backbone. Cancer detection results are reported in the following Table 6. **Our method improves TranUNet’s baseline performance by 6.2%, demonstrating its generalizability to other backbones.**\n\nTable 6. Cancer detection (AP and AUROC) with TransUNet as the backbone on the rationale dataset.\n| | AP | AUROC | Average |\n|---|:---:|:---:|:---:|\n| Baseline | 0.416 | 0.811 | 0.614 |\n| Ours | **0.463** | **0.840** | **0.652** |\n\n> **Q6:** Is there a direct comparison between MedSAM and your rationale dataset? \n\n**A6:** MedSAM requires the user to manually input a bounding box to indicate the region of interest, so we can't directly compare it to our method, which doesn't require these prompts. Instead, we compared our model with other medical SAM-based methods (like MA-SAM and SAMed) that don't need prompts for fair comparisons. The results show that our model achieves significantly better performance, demonstrating the clear benefits of our method.\n\n> **Q7:** Could gaze data be integrated into your approach to further enhance grounding?\n\n**A7:** Thanks for the insightful suggestion. Gaze tracking could indeed enhance our framework. Gaze heatmaps could help validate whether the clinical concepts our model highlights align with regions where experts focus their attention during diagnosis. We noticed that one work [2] discussed setting up the eye-tracking system for the prostate cancer diagnosis. In future work, we will find out if there are open-source datasets with gaze data and explore using them to build more powerful models.\n\n> **Q8:** Discussion of the mentioned reference.\n\n**A8:** Thanks for highlighting these relevant references. We agree that these works closely relate to our approach of grounding clinical concepts. We'll add PadChest and PadChest-GR (2024) to the related work section in the revised version.\n\n*References:*\n\n[1] Saha et al. \"Artificial intelligence and radiologists in prostate cancer detection on MRI: an international, paired, non-inferiority, confirmatory study.\" The Lancet Oncology 2024.\n\n[2] Celik et al. \"Eye Tracking System for Prostate Cancer Diagnosis Using Multi-Parametric MRI.\"", "claims_and_evidence": "The authors mostly backed up their claims with evaluations. However, some claims lack explicit evidence, as detailed in the questions below.", "methods_and_evaluation_criteria": "The methods and evaluation criteria are clearly presented and justified.", "theoretical_claims": "The theoretical claims have been checked and appear to be correct.", "experimental_designs_or_analyses": "Soundness/validity of any experimental designs or analyses are checked.", "relation_to_broader_scientific_literature": "The topic is important, and the paper makes a valuable contribution to explainable AI in medical imaging.", "essential_references_not_discussed": "The paper does not discuss relevant works such as: PadChest (2020): A large-scale dataset of over 160,000 Spanish chest X-rays with radiology reports and multi-label annotations. The recent PadChest-GR (2024) extension provides 4,555 images with grounded reports linking findings to bounding boxes, which directly relates to the authors' goal of grounding clinical concepts in images.", "other_strengths_and_weaknesses": "Strengths:\n- The curated rationale dataset is a significant contribution to improving model explainability.\n- The proposed self-supervised optimization method is innovative and addresses the need for fine-grained concept localization.\n- The model demonstrates superior performance in segmentation and detection tasks.\n\nWeaknesses:\n- The disentanglement constraint assumes strict spatial separation of clinical concepts, which may not always hold due to overlapping or co-existing conditions.\n- Lack of discussion on how to handle diffuse diseases that affect the entire image.\n- Some experimental results require further clarification (see questions below).", "other_comments_or_suggestions": "There is a small typo in line 220: \"resample(.)\"", "questions_for_authors": "- Loss Function Weighting: Do the Dice loss and InfoNCE loss have the same weight in Eq. 2? If so, can you elaborate on this decision?\n- Disentanglement Constraint: The claim \"clinically different concepts should highlight different regions in the image\" does not always hold. Some diseases affect multiple regions or share visual features (e.g., pulmonary edema and pneumonia in X-rays), while others are diffuse. The localization constraint may address the first case, but how do you handle the second? Can you clarify how the model adapts to conditions requiring multiple highlighted areas?\n- Comprehensiveness Score: Why is comprehensiveness scored lower than other criteria in Section 5.2?\n- Table 2 - Second Average Column: What does the second \"average\" column in Table 2 represent? If it averages precision and AUROC, what is the motivation for this, given that they are different metrics?\n- Backbone Choice: Why was MedSAM chosen as the backbone instead of U-Net? Have you compared performance between the two architectures?\n- MedSAM Comparison: Is there a direct comparison between MedSAM and your rationale dataset? If so, what benefits does the rationale dataset provide in conjunction with MedSAM?\n- Gaze Information: In similar works using different imaging modalities, gaze tracking has been incorporated for model explainability. Could gaze data be integrated into your approach to further enhance grounding?", "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:

Meta-review: This is an interesting paper that addresses the challenge of enhancing interpretability in medical AI models for tumor detection and segmentation. The authors curate a dataset of 180K image-mask-rationale triples, verified by expert radiologists, and develop a self-supervised model that disentangles and localizes fine-grained clinical concepts without requiring pixel-level annotations.

Strengths of this work are that the curated dataset is a significant contribution to improving model explainability,
the proposed self-supervised optimization method is innovative and addresses the need for fine-grained concept localization,
the model demonstrates superior performance in segmentation and detection tasks. Weaknesses contain lack of discussion on how to handle diffuse diseases that affect the entire image, and some experimental results require further clarification (see questions below).

In total the strengths overweight the weaknesses here. It will be important that the authors include the failure case analysis in the final version, as promised in the rebuttal.
Decision: Accept (poster)