Multi Anatomy X-Ray Foundation Model

• URL: http://arxiv.org/abs/2509.12146v1
• Date: Mon, 15 Sep 2025 17:12:26 GMT
• Title: Multi Anatomy X-Ray Foundation Model
• Authors: Nishank Singla, Krisztian Koos, Farzin Haddadpour, Amin Honarmandi Shandiz, Lovish Chum, Xiaojian Xu, Qing Jin, Erhan Bas,
• Abstract summary: We introduce XR-0, the multi-anatomy X-ray foundation model using self-supervised learning.<n> XR-0 achieves state-of-the-art performance on most multi-anatomy tasks and remains competitive on chest-specific benchmarks.
• Score: 7.079609136804425
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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