The diagnosis and treatment of chest diseases play a crucial role in maintaining human health. X-ray examination has become the most common clinical examination means due to its efficiency and cost-effectiveness. Artificial intelligence analysis methods for chest X-ray images are limited by insufficient annotation data and varying levels of annotation, resulting in weak generalization ability and difficulty in clinical dissemination. Here we present EVA-X, an innovative foundational model based on X-ray images with broad applicability to various chest disease detection tasks. EVA-X is the first X-ray image based self-supervised learning method capable of capturing both semantic and geometric information from unlabeled images for universal X-ray image representation. Through extensive experimentation, EVA-X has demonstrated exceptional performance in chest disease analysis and localization, becoming the first model capable of spanning over 20 different chest diseases and achieving leading results in over 11 different detection tasks in the medical field. Additionally, EVA-X significantly reduces the burden of data annotation in the medical AI field, showcasing strong potential in the domain of few-shot learning. The emergence of EVA-X will greatly propel the development and application of foundational medical models, bringing about revolutionary changes in future medical research and clinical practice. Our codes and models are available at: https://github.com/hustvl/EVA-X.

翻译：胸部疾病的诊断与治疗对维护人类健康至关重要。X光检查因其高效性和经济性已成为最常用的临床检查手段。基于人工智能的胸部X光图像分析方法受制于标注数据不足和标注质量参差不齐，导致泛化能力弱且难以在临床推广。本文提出EVA-X——一种基于X光图像的创新基础模型，广泛适用于多种胸部疾病检测任务。EVA-X是首个基于X光图像的自监督学习方法，能够从无标注图像中同时捕获语义和几何信息，实现通用X光图像表征。通过大量实验，EVA-X在胸部疾病分析与定位方面展现出卓越性能，成为首个覆盖20余种胸部疾病并在医学领域11项不同检测任务中取得领先结果的模型。此外，EVA-X显著降低了医学AI领域的数据标注负担，在小样本学习领域展现出强大潜力。EVA-X的出现将极大推动医学基础模型的发展与应用，为未来医学研究与临床实践带来革命性变革。我们的代码与模型见：https://github.com/hustvl/EVA-X。