Embedding Task Knowledge into 3D Neural Networks via Self-supervised Learning
This addresses the challenge of expensive annotation in medical imaging, though it is incremental as it builds on existing self-supervised and contrastive learning techniques.
The paper tackles the problem of limited annotated data in 3D medical image classification by proposing a self-supervised learning method that embeds task-specific lesion knowledge, achieving effective results on public and private datasets.
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.