Multi Anatomy X-Ray Foundation Model
This work addresses the need for robust, general-purpose medical vision models in radiology by overcoming the limitation of existing models that are restricted to chest anatomy.
The paper tackled the problem of limited generalization in AI foundation models for X-ray imaging by introducing XR-0, a multi-anatomy model trained on 1.15 million images, which achieved state-of-the-art performance on most multi-anatomy tasks and remained competitive on chest-specific benchmarks.
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.