arxiv: 2604.19350 · v1 · submitted 2026-04-21 · 💻 cs.CV

Recognition: unknown

Attend what matters: Leveraging vision foundational models for breast cancer classification using mammograms

Samyak Sanghvi , Piyush Miglani , Sarvesh Shashikumar , Kaustubh R Borgavi , Veenu Singla , Chetan Arora

Authors on Pith no claims yet

Pith reviewed 2026-05-10 02:35 UTC · model grok-4.3

classification 💻 cs.CV

keywords mammogram classificationbreast cancer detectionvision transformerregion of interestcontrastive learningDINOv2medical image analysiscomputer-aided diagnosis

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The pith

RoI-based token reduction and contrastive learning on DINOv2 ViT features let Vision Transformers classify breast cancer from mammograms more accurately than standard approaches by focusing on small lesions and handling fine-grained class差异

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Vision Transformers underperform on mammograms because high-resolution scans produce too many tokens for softmax attention to localize small abnormalities, and because the task involves subtle distinctions between classes that standard cross-entropy training does not capture well. The paper shows that an object-detection model can first identify and retain only the relevant regions of interest, slashing the token count and directing attention to potential lesions. Contrastive learning is then applied across these selected regions using hard negatives to sharpen discrimination, while swapping in DINOv2 pretraining supplies localization-aware features instead of global CLIP-style representations. Public-dataset experiments indicate the combined pipeline beats existing baselines. A reader would care because reliable automated analysis could scale breast-cancer screening where radiologist time is limited.

Core claim

The paper claims that its three-part framework—object-detection-guided RoI selection to reduce tokens, contrastive training on the retained RoIs to improve fine-grained separation, and a DINOv2-pretrained ViT for localization-sensitive features—directly solves the token overload and intra-class variability problems that limit standard ViTs on mammograms, producing higher classification performance than baselines on public mammography datasets.

What carries the argument

RoI token reduction driven by an off-the-shelf object detector, followed by hard-negative contrastive learning on the selected patches inside a DINOv2 ViT

If this is right

Token count is reduced to only the detected RoIs, allowing the ViT attention to localize small lesions that would otherwise be diluted across thousands of background patches.
Hard-negative contrastive pairs drawn from the RoIs teach the model to separate cases that standard cross-entropy treats as too similar.
DINOv2 pretraining supplies features that already encode localization cues, avoiding the global averaging bias of CLIP-style embeddings.
The resulting accuracy gains on public mammography datasets establish the pipeline's effectiveness relative to prior ViT and CNN baselines.
The same design points toward practical use in large-scale automated screening workflows.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

If the detector stage proves robust, the same token-reduction-plus-contrastive pattern could transfer to other high-resolution medical tasks such as lung-nodule detection in CT.
Token pruning may cut both memory and compute enough to support deployment on hospital workstations without specialized hardware.
End-to-end joint training of the detector and classifier could be tested to limit error propagation from missed lesions.
The emphasis on DINOv2 over CLIP suggests that self-supervised localization pretraining is worth checking for other medical imaging domains where global features fall short.

Load-bearing premise

An off-the-shelf object detector can reliably locate the small abnormalities in mammograms without missing lesions or injecting noise that harms downstream classification, and contrastive learning on those RoIs will add meaningful fine-grained signal beyond ordinary cross-entropy training.

What would settle it

Running the full pipeline on a held-out mammogram set where the object detector's lesion recall is measured below 70 percent and confirming that overall classification accuracy then falls below a plain ViT baseline trained with cross-entropy.

read the original abstract

Vision Transformers $(\texttt{ViT})$ have become the architecture of choice for many computer vision tasks, yet their performance in computer-aided diagnostics remains limited. Focusing on breast cancer detection from mammograms, we identify two main causes for this shortfall. First, medical images are high-resolution with small abnormalities, leading to an excessive number of tokens and making it difficult for the softmax-based attention to localize and attend to relevant regions. Second, medical image classification is inherently fine-grained, with low inter-class and high intra-class variability, where standard cross-entropy training is insufficient. To overcome these challenges, we propose a framework with three key components: (1) Region of interest $(\texttt{RoI})$ based token reduction using an object detection model to guide attention; (2) contrastive learning between selected $\texttt{RoI}$ to enhance fine-grained discrimination through hard-negative based training; and (3) a $\texttt{DINOv2}$ pretrained $\texttt{ViT}$ that captures localization-aware, fine-grained features instead of global $\texttt{CLIP}$ representations. Experiments on public mammography datasets demonstrate that our method achieves superior performance over existing baselines, establishing its effectiveness and potential clinical utility for large-scale breast cancer screening. Our code is available for reproducibility here: https://aih-iitd.github.io/publications/attend-what-matters

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper assembles RoI token pruning via an off-the-shelf detector, hard-negative contrastive learning on the selected patches, and DINOv2 pretraining into one pipeline for mammogram classification, but the abstract supplies no metrics and the detector's reliability on small lesions stays unverified.

read the letter

Colleague, The punchline here is that the authors have assembled a three-part pipeline for handling high-resolution mammograms with vision transformers: use an off-the-shelf object detector to drop most tokens down to RoIs, run contrastive learning with hard negatives on those selected patches, and initialize with DINOv2 rather than CLIP. The abstract claims this beats baselines on public data. What stands out as new is the specific combination for this domain. Each piece exists separately, but applying detector-guided pruning plus targeted contrastive training to mammograms is a reasonable engineering step. The code link is helpful and shows they care about reproducibility. The paper does a decent job laying out the problems with standard ViTs on medical images—token overload and the fine-grained nature of the task. That part is clear. The soft spots are more substantial. The abstract gives no quantitative results at all—no accuracies, no dataset details, no ablations showing what each component adds. Without those, it's impossible to tell if the method actually delivers. The stress-test concern about the detector is on point: if the detector misses small lesions, which are common in mammograms, then the token reduction step discards important information and everything downstream suffers. The abstract does not say whether they fine-tuned the detector or measured its performance on lesion annotations. That seems like a load-bearing assumption that needs checking in the full paper. This work is aimed at researchers and engineers building computer-aided diagnosis tools for breast cancer screening. Someone looking for practical ways to make ViTs more efficient on high-res medical images could find the pipeline description useful, especially with the code available. I would send this to peer review. The idea is solid enough on paper and the reproducibility helps, so referees can evaluate the actual experiments and point out any gaps in the detector validation or results reporting.

Referee Report

2 major / 2 minor

Summary. The manuscript identifies limitations of Vision Transformers on high-resolution mammograms (excessive tokens from small abnormalities and insufficient fine-grained discrimination under cross-entropy). It proposes a three-part framework: (1) RoI-based token reduction via an off-the-shelf object detection model, (2) contrastive learning on the selected RoIs using hard negatives, and (3) a DINOv2-pretrained ViT backbone. Experiments on public mammography datasets are stated to demonstrate superior performance over baselines, with code released for reproducibility.

Significance. If the reported gains hold after validation of the detector stage, the work offers a practical route to adapt large foundational ViTs to high-resolution medical images by pruning irrelevant tokens and strengthening fine-grained separation. The explicit code release is a clear strength that supports reproducibility and potential follow-up studies.

major comments (2)

[Abstract] Abstract: the central claim that the method 'achieves superior performance over existing baselines' is presented without any quantitative metrics, dataset sizes, confidence intervals, ablation results, or statistical tests, preventing verification of the empirical contribution.
[Method] Method section (RoI token reduction component): the pipeline depends on an off-the-shelf object detector to retain lesion-containing tokens, yet no localization metrics (recall, precision, or IoU on annotated lesions) are reported for the mammography data; if recall on small low-contrast abnormalities is low, relevant tokens are discarded before the ViT and contrastive stages, rendering downstream improvements moot.

minor comments (2)

[Abstract] Abstract: the public datasets are referred to generically; naming them (e.g., INBreast, CBIS-DDSM) and stating their sizes would improve immediate readability.
[Method] The contrastive loss temperature and margin are listed as free parameters; a brief sensitivity analysis or default values would clarify reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback on our manuscript. We have addressed each of the major comments point by point below. Where appropriate, we will revise the manuscript to incorporate the suggestions and improve clarity and rigor.

read point-by-point responses

Referee: [Abstract] Abstract: the central claim that the method 'achieves superior performance over existing baselines' is presented without any quantitative metrics, dataset sizes, confidence intervals, ablation results, or statistical tests, preventing verification of the empirical contribution.

Authors: We agree that including quantitative details in the abstract would strengthen it. In the revised manuscript, we will update the abstract to include key performance metrics (e.g., AUC improvements on INBreast and CBIS-DDSM), dataset sizes, and references to ablation studies and statistical significance tests reported in the experiments section. This will enable better verification of the empirical contributions without exceeding the abstract length limits. revision: yes
Referee: [Method] Method section (RoI token reduction component): the pipeline depends on an off-the-shelf object detector to retain lesion-containing tokens, yet no localization metrics (recall, precision, or IoU on annotated lesions) are reported for the mammography data; if recall on small low-contrast abnormalities is low, relevant tokens are discarded before the ViT and contrastive stages, rendering downstream improvements moot.

Authors: We acknowledge the importance of validating the RoI detection stage. The detector is an off-the-shelf model not specifically trained on mammography data, and the primary datasets used (INBreast, CBIS-DDSM) provide limited or no bounding box annotations for comprehensive IoU or precision-recall evaluation. We will revise the method section to include a discussion of this limitation, add qualitative examples of RoI token selection, and report any available proxy metrics such as the average number of tokens retained. Additionally, we will analyze the sensitivity of the final classification performance to variations in RoI quality through controlled experiments. This should mitigate concerns about the detector's impact. revision: partial

Circularity Check

0 steps flagged

No circularity; purely empirical method with no derivations or reductions

full rationale

The paper proposes a practical framework combining RoI selection via an off-the-shelf object detector, contrastive learning on selected patches, and a DINOv2 ViT backbone, then evaluates it empirically on public mammography datasets against baselines. No equations, mathematical derivations, or first-principles claims appear in the provided text. The superiority claim rests on experimental performance numbers rather than any chain that reduces by construction to fitted parameters, self-definitions, or self-citations. The detector and contrastive components are treated as modular inputs whose effectiveness is tested downstream, not derived from the target result. This is a standard non-circular empirical ML paper.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

Only the abstract is available, so the ledger is necessarily incomplete. The approach relies on pretrained foundational models (DINOv2, object detector) whose internal assumptions are taken from prior literature. No new physical entities are postulated.

free parameters (2)

number of retained RoI tokens
Chosen to balance compute and coverage; value not stated in abstract
contrastive loss temperature and margin
Standard hyperparameters in contrastive training; not reported

axioms (2)

domain assumption DINOv2 embeddings are more localization-aware and fine-grained than CLIP embeddings for medical images
Invoked when the authors choose DINOv2 over CLIP representations
domain assumption Object detection model accurately identifies clinically relevant regions in mammograms
Required for the token-reduction step to preserve diagnostic information

pith-pipeline@v0.9.0 · 5568 in / 1391 out tokens · 39979 ms · 2026-05-10T02:35:03.250454+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

30 extracted references · 4 canonical work pages · 2 internal anchors

[1]

INTRODUCTION Background.Mammography is a low-dose X-ray imaging modality that serves as the most widely adopted screening procedure for the early detection of breast cancer [3], the most common malignancy among women, accounting for more than 685,000 deaths worldwide in 2020 [4]. As the gold standard for detecting breast malignancies [3], mammograms provi...

work page internal anchor Pith review Pith/arXiv arXiv 2020
[2]

We employ an object detection module as a preproces- sor for full scale mammograms in order to obtainRoIs, which result in fewer tokens that need to be attended to by the classification head
[3]

This leverages the insight that medical abnormalities are typically localized within a singleRoI, allowing the model to learn from hard negatives effectively

To improve fine-grained classification, we use contrastive training between the selectedRoIs. This leverages the insight that medical abnormalities are typically localized within a singleRoI, allowing the model to learn from hard negatives effectively
[4]

Instead of using aCLIP[2] pretrained Vision Transformer (ViT) trained for global feature extraction, we adopt a DINOv2[1] pretrainedViT, which is trained on multiple localization tasks to extract fine-grained, local features
[5]

Comparison of baselines on public mammography datasets establish the framework’s efficiency, adaptability, and clinical utility for large-scale breast cancer screening ap- plications. Our proposed approach achieves an increment of1%on AUC and a remarkable4%gain over previ- ously reported state-of-the-art classifier, which requires an image-text pretrainin...
[6]

Attention betweenRoIs.As shown in Figure 1, we ex- tract theRoIusingG-DINO[11] and subsequently employ DINOv2[1] for feature extraction

METHODOLOGY Problem Statement.Our goal in this study is to develop a classification frameworkf(x)that maps 2D mammograms (x∈ X) to binary labels indicating the presence or absence of breast cancer, whereXdenotes a mammography dataset. Attention betweenRoIs.As shown in Figure 1, we ex- tract theRoIusingG-DINO[11] and subsequently employ DINOv2[1] for featu...
[7]

RESULTS In Table 1, we compare our results with those of other unimodal image-based and multimodal image-text-based baselines, with the previous state-of-the-art (SOTA) being Mammo-CLIP[19]. Despite being a unimodal approach, our scores demonstrate a4%improvement on the F1 score and a1%gain on theAUCover thisSOTA, which relies on CLIP[2] -style pretrainin...
[8]

Lastly, adding a repulsive contrastive loss to separate dis- similarRoIsboosted both the F1 score andAUCby1%, achieving our best performance

to encode spatial information of non-consecutive, non- adjacentRoIsfurther increased the F1 score by almost2%. Lastly, adding a repulsive contrastive loss to separate dis- similarRoIsboosted both the F1 score andAUCby1%, achieving our best performance
[9]

CONCLUSION In this study, investigated the lower performance of trans- former models in medical imaging, and came up with large number of tokens due to high resolution, and fine-grained na- ture of the problem as the reasons. We presented a novel ar- chitecture based onRoIbased token selection, contrastive loss based hard negative training, and upgradedVi...
[10]

Eth- ical approval was not required as confirmed by the license attached with the open access data

COMPLIANCE WITH ETHICAL STANDARDS This research was conducted retrospectively using human subject data obtained from an open-access source [20]. Eth- ical approval was not required as confirmed by the license attached with the open access data
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