A Versatile Foundation Model for AI-enabled Mammogram Interpretation

[2509.20271] A Versatile Foundation Model for AI-enabled Mammogram Interpretation Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2509.20271 (cs) [Submitted on 24 Sep 2025 ( v1 ), last revised 14 Aug 2026 (this version, v2)] Title: A Versatile Foundation Model for AI-enabled Mammogram Interpretation Authors: Fuxiang Huang , Jiayi Zhu , Yunfang Yu , Yu Xie , Yuan Guo , Qingcong Kong , Mingxiang Wu , Xinrui Jiang , Shu Yang , Jiabo Ma , Ziyi Liu , Zhe Xu , Zhixuan Chen , Yujie Tan , Zifan He , Luhui Mao , Xi Wang , Junlin Hou , Lei Zhang , Qiong Luo , Zhenhui Li , Herui Yao , Hao Chen View a PDF of the paper titled A Versatile Foundation Model for AI-enabled Mammogram Interpretation, by Fuxiang Huang and 22 other authors View PDF HTML (experimental) Abstract: Breast cancer is the most commonly diagnosed cancer and the leading cause of cancer-related mortality in women globally. Mammography is essential for the early detection and diagnosis of breast lesions. Despite recent progress in foundation models (FMs) for mammogram analysis, their clinical translation remains constrained by several fundamental limitations, including insufficient diversity in training data, limited model generalizability, and a lack of comprehensive evaluation across clinically relevant tasks. Here, we introduce VersaMammo, a versatile foundation model for mammograms, designed to overcome these limitations. We curated the largest multi-institutional mammogram dataset to date, comprising 706,239 images from 21 sources. To improve generalization, we propose a two-stage pre-training strategy to develop VersaMammo, a mammogram foundation model. First, a teacher model is trained via self-supervised learning to extract transferable features from unlabeled mammograms. Then, supervised learning combined with knowledge distillation transfers both features and clinical knowledge into VersaMammo. To ensure a comprehensive evaluation, we established a benchmark comprising 92 specific tasks, including 68 internal tasks and 24 external validation tasks, spanning 5 major clinical task categories: lesion detection, segmentation, classification, image retrieval, and visual question answering. VersaMammo achieves state-of-the-art performance, ranking first in 50 out of 68 specific internal tasks and 20 out of 24 external validation tasks, with average ranks of 1.5 and 1.2, respectively. These results demonstrate its superior generalization and clinical utility, offering a substantial advancement toward reliable and scalable breast cancer screening and diagnosis. Comments: 69 pages, 12 figures, 52 tables Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2509.20271 [cs.CV] (or arXiv:2509.20271v2 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2509.20271 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Fuxiang Huang [ view email ] [v1] Wed, 24 Sep 2025 16:04:39 UTC (3,134 KB) [v2] Fri, 14 Aug 2026 19:07:45 UTC (22,557 KB) Full-text links: Access Paper: View a PDF of the paper titled A Versatile Foundation Model for AI-enabled Mammogram Interpretation, by Fuxiang Huang and 22 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2025-09 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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