CheSS: Chest X-Ray Pre-trained Model via Self-supervised Contrastive Learning.

ABSTRACT: Training deep learning models on medical images heavily depends on experts' expensive and laborious manual labels. In addition, these images, labels, and even models themselves are not widely publicly accessible and suffer from various kinds of bias and imbalances. In this paper, chest X-ray pre-trained model via self-supervised contrastive learning (CheSS) was proposed to learn models with various representations in chest radiographs (CXRs). Our contribution is a publicly accessible pretrained model trained with a 4.8-M CXR dataset using self-supervised learning with a contrastive learning and its validation with various kinds of downstream tasks including classification on the 6-class diseases in internal dataset, diseases classification in CheXpert, bone suppression, and nodule generati

SUBMITTER: Cho K 

PROVIDER: S-EPMC10287612 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature