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Solving the Needle-in-a-Haystack Problem in Mammography Vision-Language Model with Differentiable Subset Sampling
Youngseok Kim, Author 2, Author 3, Author 4, Author 5
multimodalvision-languagemedical imagingdeep learning
2609.03085
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1h ago80%
Abstract
This paper presents TopKSigLIP, a vision-language model that improves mammography analysis by addressing high-resolution data and report homogeneity issues.
Reality Card
Core Claim
TopKSigLIP outperforms existing models in mammography tasks such as density assessment and cancer prediction under zero-shot evaluation.
Method / Result
TopKSigLIP achieves superior lesion localization compared to post-hoc Grad-CAM.
Limitations
The model's performance may be limited by the availability of high-quality mammography datasets for training and evaluation.
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