  Deep learning highly relies on the amount of annotated data. However,
annotating medical images is extremely laborious and expensive. To this end,
self-supervised learning (SSL), as a potential solution for deficient annotated
data, attracts increasing attentions from the community. However, SSL
approaches often design a proxy task that is not necessarily related to target
task. In this paper, we propose a novel SSL approach for 3D medical image
classification, namely Task-related Contrastive Prediction Coding (TCPC), which
embeds task knowledge into training 3D neural networks. The proposed TCPC first
locates the initial candidate lesions via supervoxel estimation using simple
linear iterative clustering. Then, we extract features from the sub-volume
cropped around potential lesion areas, and construct a calibrated contrastive
predictive coding scheme for self-supervised learning. Extensive experiments
are conducted on public and private datasets. The experimental results
demonstrate the effectiveness of embedding lesion-related prior-knowledge into
neural networks for 3D medical image classification.
