DrasCLR: A Self-supervised Framework of Learning Disease-related and Anatomy-specific Representation for 3D Lung CT Images

Ke Yu, Li Sun, Junxiang Chen, Max Reynolds, Tigmanshu Chaudhary, kayhan Batmanghelich

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 images to overcome these challenges. We propose
two domain-specific contrastive learning strategies: one aims to capture subtle disease
patterns inside a local anatomical region, and the other aims to represent severe disease
patterns that span larger regions. We formulate the encoder using conditional
hyper-parameterized network, in which the parameters are dependant on the anatomical
location, to extract anatomically sensitive features. Extensive experiments on largescale
datasets of lung CT scans show that our method improves the performance of
many downstream prediction and segmentation tasks. The patient-level representation
improves the performance of the patient survival prediction task. We show how our
method can detect emphysema subtypes via dense prediction. We demonstrate that
fine-tuning the pre-trained model can significantly reduce annotation efforts without
sacrificing emphysema detection accuracy. Our ablation study highlights the importance
of incorporating anatomical context into the SSL framework.