DU-Net based Unsupervised Contrastive Learning for Cancer Segmentation in Histology Images
This addresses the problem of lacking annotated training data for cancer segmentation in medical imaging, though it is incremental as it builds on existing unsupervised and contrastive learning methods.
The paper tackles unsupervised cancer segmentation in histology images by developing a contrastive learning framework with a Deep U-Net encoder and data augmentation, achieving competitive performance that surpasses some supervised networks.
In this paper, we introduce an unsupervised cancer segmentation framework for histology images. The framework involves an effective contrastive learning scheme for extracting distinctive visual representations for segmentation. The encoder is a Deep U-Net (DU-Net) structure that contains an extra fully convolution layer compared to the normal U-Net. A contrastive learning scheme is developed to solve the problem of lacking training sets with high-quality annotations on tumour boundaries. A specific set of data augmentation techniques are employed to improve the discriminability of the learned colour features from contrastive learning. Smoothing and noise elimination are conducted using convolutional Conditional Random Fields. The experiments demonstrate competitive performance in segmentation even better than some popular supervised networks.