MammoDINO: Anatomically Aware Self-Supervision for Mammographic Images
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909843620626432 |
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| author | Zhou, Sicheng Wu, Lei Xiao, Cao Bhatia, Parminder Kass-Hout, Taha |
| author_facet | Zhou, Sicheng Wu, Lei Xiao, Cao Bhatia, Parminder Kass-Hout, Taha |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_11883 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MammoDINO: Anatomically Aware Self-Supervision for Mammographic Images Zhou, Sicheng Wu, Lei Xiao, Cao Bhatia, Parminder Kass-Hout, Taha Computer Vision and Pattern Recognition Artificial Intelligence 1.2 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. |
| title | MammoDINO: Anatomically Aware Self-Supervision for Mammographic Images |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence 1.2 |
| url | https://arxiv.org/abs/2510.11883 |