构建首个覆盖多部位的X光基础模型，提升医疗影像AI泛化能力
Multi Anatomy X-Ray Foundation Model
- 基于115万张跨部位X光图自监督训练
- 在12个数据集20项任务中达领先水平
- 适合需要多部位通用诊断的临床AI研发
X射线成像在放射科广泛应用，但现有AI基础模型大多仅限于胸部解剖，难以跨任务泛化。本文提出XR-0，一个基于115万张涵盖多种解剖区域的私有数据集，通过自监督学习训练的多部位X光基础模型，在12个数据集和20项下游任务（包括分类、检索、分割、定位、视觉定位和报告生成）上进行评估。XR-0在多数多部位任务中达到当前最优性能，且在胸部专用基准上仍具竞争力。结果表明，解剖多样性与监督策略对构建鲁棒、通用的医学视觉模型至关重要，为放射科可扩展、可适配的AI系统铺平道路。
原文摘要 · Abstract (English)
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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