{"ID":2871044,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.12146","arxiv_id":"2509.12146","title":"Multi Anatomy X-Ray Foundation Model","abstract":"X-ray imaging is a ubiquitous in radiology, yet most existing AI foundation models are limited to chest anatomy and fail to generalize across broader clinical tasks. In this work, we introduce XR-0, the multi-anatomy X-ray foundation model using self-supervised learning on a large, private dataset of 1.15 million images spanning diverse anatomical regions and evaluated across 12 datasets and 20 downstream tasks, including classification, retrieval, segmentation, localization, visual grounding, and report generation. XR-0 achieves state-of-the-art performance on most multi-anatomy tasks and remains competitive on chest-specific benchmarks. Our results demonstrate that anatomical diversity and supervision are critical for building robust, general-purpose medical vision models, paving the way for scalable and adaptable AI systems in radiology.","short_abstract":"X-ray imaging is a ubiquitous in radiology, yet most existing AI foundation models are limited to chest anatomy and fail to generalize across broader clinical tasks. In this work, we introduce XR-0, the multi-anatomy X-ray foundation model using self-supervised learning on a large, private dataset of 1.15 million image...","url_abs":"https://arxiv.org/abs/2509.12146","url_pdf":"https://arxiv.org/pdf/2509.12146v2","authors":"[\"Nishank Singla\",\"Krisztian Koos\",\"Farzin Haddadpour\",\"Amin Honarmandi Shandiz\",\"Lovish Chum\",\"Xiaojian Xu\",\"Qing Jin\",\"Erhan Bas\"]","published":"2025-09-15T17:12:26Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[]","has_code":false}