DrasCLR: A self-supervised framework of learning disease-related and anatomy-specific representation for 3D lung CT images.

ABSTRACT: Large-scale volumetric medical images with annotation are rare, costly, and time prohibitive to acquire. Self-supervised learning (SSL) offers a promising pre-training and feature extraction solution for many downstream tasks, as it only uses unlabeled data. Recently, SSL methods based on instance discrimination have gained popularity in the medical imaging domain. However, SSL pre-trained encoders may use many clues in the image to discriminate an instance that are not necessarily disease-related. Moreover, pathological patterns are often subtle and heterogeneous, requiring the ability of the desired method to represent anatomy-specific features that are sensitive to abnormal changes in different body parts. In this work, we present a novel SSL framework, named DrasCLR, for 3D lung CT ima

SUBMITTER: Yu K 

PROVIDER: S-EPMC10872608 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature