mlsanityData to 14 Oct 2026

Multi Anatomy X-Ray Foundation Model

arXiv 2509.12146 ↗First posted 15 Sep 2025cs.CV cs.AIcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 41 of its 52 references that are in the map. First posted on arXiv 15 Sep 2025.

Builds on

41 of 52 references.

References · 41

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026A4-MR: Abnormality-aware Anatomical Alignment for MRcited by 0