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arXiv · 2510.11883

MammoDINO: Anatomically Aware Self-Supervision for Mammographic Images

Sicheng Zhou·Lei Wu·Cao Xiao·Parminder Bhatia·Taha Kass-Hout
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Abstract

Self-supervised learning (SSL) has transformed vision encoder training in general domains but remains underutilized in medical imaging due to limited data and domain specific biases. We present MammoDINO, a novel SSL framework for mammography, pretrained on 1.4 million mammographic images. To capture clinically meaningful features, we introduce a breast tissue aware data augmentation sampler for both image-level and patch-level supervision and a cross-slice contrastive learning objective that leverages 3D digital breast tomosynthesis (DBT) structure into 2D pretraining. MammoDINO achieves state-of-the-art performance on multiple breast cancer screening tasks and generalizes well across five benchmark datasets. It offers a scalable, annotation-free foundation for multipurpose computer-aided diagnosis (CAD) tools for mammogram, helping reduce radiologists' workload and improve diagnostic efficiency in breast cancer screening.

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Sicheng Zhou, Lei Wu, Cao Xiao, Parminder Bhatia, Taha Kass-Hout. 2025-10-13. MammoDINO: Anatomically Aware Self-Supervision for Mammographic Images. https://arxiv.org/abs/2510.11883

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