EVA-X: a foundation model for general chest x-ray analysis with self-supervised learning.

ABSTRACT: 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. EVA-X uses a self-supervised learning method capable of capturing both semantic and geometric information from unlabeled images for universal X-ray image representation. It has demonstrated exceptional performance in chest disease analysis and localization, becoming a model capable of spanning over 20 different chest pathologies and achieving leading results in over 11 different pathology detection tasks. Additionally, EVA-X significantly reduces the burden of data

SUBMITTER: Yao J 

PROVIDER: S-EPMC12624024 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature