Multi-Scale Cross Contrastive Learning for Semi-Supervised Medical Image Segmentation

• URL: http://arxiv.org/abs/2306.14293v1
• Date: Sun, 25 Jun 2023 16:55:32 GMT
• Title: Multi-Scale Cross Contrastive Learning for Semi-Supervised Medical Image Segmentation
• Authors: Qianying Liu, Xiao Gu, Paul Henderson, Fani Deligianni
• Abstract summary: We develop a novel Multi-Scale Cross Supervised Contrastive Learning framework to segment structures in medical images. Our approach contrasts multi-scale features based on ground-truth and cross-predicted labels, in order to extract robust feature representations. It outperforms state-of-the-art semi-supervised methods by more than 3.0% in Dice.
• Score: 14.536384387956527
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semi-supervised learning has demonstrated great potential in medical image segmentation by utilizing knowledge from unlabeled data. However, most existing approaches do not explicitly capture high-level semantic relations between distant regions, which limits their performance. In this paper, we focus on representation learning for semi-supervised learning, by developing a novel Multi-Scale Cross Supervised Contrastive Learning (MCSC) framework, to segment structures in medical images. We jointly train CNN and Transformer models, regularising their features to be semantically consistent across different scales. Our approach contrasts multi-scale features based on ground-truth and cross-predicted labels, in order to extract robust feature representations that reflect intra- and inter-slice relationships across the whole dataset. To tackle class imbalance, we take into account the prevalence of each class to guide contrastive learning and ensure that features adequately capture infrequent classes. Extensive experiments on two multi-structure medical segmentation datasets demonstrate the effectiveness of MCSC. It not only outperforms state-of-the-art semi-supervised methods by more than 3.0% in Dice, but also greatly reduces the performance gap with fully supervised methods.

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