  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 medical imaging 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 large-scale computer
tomography (CT) datasets of lung images 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.
