Mammo-FM: Breast-specific foundational model for Integrated Mammographic Diagnosis, Prognosis, and Reporting
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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| author | Ghosh, Shantanu Joshi, Vedant Parthesh Syed, Rayan Budhraja, Param Kassem, Aya Morrison, Katelyn C. Tang, Alex Wong, Ho Cheung Aiden Varshney, Abhishek Basak, Payel Dai, Weicheng Gichoya, Judy Wawira Trivedi, Hari M. Banerjee, Imon Visweswaran, Shyam Poynton, Clare B. Batmanghelich, Kayhan |
| author_facet | Ghosh, Shantanu Joshi, Vedant Parthesh Syed, Rayan Budhraja, Param Kassem, Aya Morrison, Katelyn C. Tang, Alex Wong, Ho Cheung Aiden Varshney, Abhishek Basak, Payel Dai, Weicheng Gichoya, Judy Wawira Trivedi, Hari M. Banerjee, Imon Visweswaran, Shyam Poynton, Clare B. Batmanghelich, Kayhan |
| contents | Breast cancer is one of the leading causes of death among women worldwide. We introduce Mammo-FM, the first foundation model specifically for mammography, pretrained on the largest and most diverse dataset to date - 140,677 patients (821,326 mammograms) across four U.S. institutions. Mammo-FM provides a unified foundation for core clinical tasks in breast imaging, including cancer diagnosis, pathology localization, structured report generation, and cancer risk prognosis within a single framework. Its alignment between images and text enables both visual and textual interpretability, improving transparency and clinical auditability, which are essential for real-world adoption. We rigorously evaluate Mammo-FM across diagnosis, prognosis, and report-generation tasks in in- and out-of-distribution datasets. Despite operating on native-resolution mammograms and using only one-third of the parameters of state-of-the-art generalist FMs, Mammo-FM consistently outperforms them across multiple public and private benchmarks. These results highlight the efficiency and value of domain-specific foundation models designed around the full spectrum of tasks within a clinical domain and emphasize the importance of rigorous, domain-aligned evaluation. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_00198 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mammo-FM: Breast-specific foundational model for Integrated Mammographic Diagnosis, Prognosis, and Reporting Ghosh, Shantanu Joshi, Vedant Parthesh Syed, Rayan Budhraja, Param Kassem, Aya Morrison, Katelyn C. Tang, Alex Wong, Ho Cheung Aiden Varshney, Abhishek Basak, Payel Dai, Weicheng Gichoya, Judy Wawira Trivedi, Hari M. Banerjee, Imon Visweswaran, Shyam Poynton, Clare B. Batmanghelich, Kayhan Computer Vision and Pattern Recognition Breast cancer is one of the leading causes of death among women worldwide. We introduce Mammo-FM, the first foundation model specifically for mammography, pretrained on the largest and most diverse dataset to date - 140,677 patients (821,326 mammograms) across four U.S. institutions. Mammo-FM provides a unified foundation for core clinical tasks in breast imaging, including cancer diagnosis, pathology localization, structured report generation, and cancer risk prognosis within a single framework. Its alignment between images and text enables both visual and textual interpretability, improving transparency and clinical auditability, which are essential for real-world adoption. We rigorously evaluate Mammo-FM across diagnosis, prognosis, and report-generation tasks in in- and out-of-distribution datasets. Despite operating on native-resolution mammograms and using only one-third of the parameters of state-of-the-art generalist FMs, Mammo-FM consistently outperforms them across multiple public and private benchmarks. These results highlight the efficiency and value of domain-specific foundation models designed around the full spectrum of tasks within a clinical domain and emphasize the importance of rigorous, domain-aligned evaluation. |
| title | Mammo-FM: Breast-specific foundational model for Integrated Mammographic Diagnosis, Prognosis, and Reporting |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.00198 |