{"ID":2835674,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.00198","arxiv_id":"2512.00198","title":"Mammo-FM: Breast-specific foundational model for Integrated Mammographic Diagnosis, Prognosis, and Reporting","abstract":"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.","short_abstract":"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 foundat...","url_abs":"https://arxiv.org/abs/2512.00198","url_pdf":"https://arxiv.org/pdf/2512.00198v3","authors":"[\"Shantanu Ghosh\",\"Vedant Parthesh Joshi\",\"Rayan Syed\",\"Param Budhraja\",\"Aya Kassem\",\"Katelyn C. Morrison\",\"Alex Tang\",\"Ho Cheung Aiden Wong\",\"Abhishek Varshney\",\"Payel Basak\",\"Weicheng Dai\",\"Judy Wawira Gichoya\",\"Hari M. Trivedi\",\"Imon Banerjee\",\"Shyam Visweswaran\",\"Clare B. Poynton\",\"Kayhan Batmanghelich\"]","published":"2025-11-28T20:41:14Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}