DU-Net based Unsupervised Contrastive Learning for Cancer Segmentation in Histology Images

• URL: http://arxiv.org/abs/2206.08791v1
• Date: Fri, 17 Jun 2022 14:04:31 GMT
• Title: DU-Net based Unsupervised Contrastive Learning for Cancer Segmentation in Histology Images
• Authors: Yilong Li, Yaqi Wang, Huiyu Zhou, Huaqiong Wang, Gangyong Jia, Qianni Zhang
• Abstract summary: The framework involves an effective contrastive learning scheme for extracting distinctive visual representations for segmentation. Experiments demonstrate competitive performance in segmentation even better than some popular supervised networks.
• Score: 14.90768557126822
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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