Multi-Scale Cross Contrastive Learning for Semi-Supervised Medical Image Segmentation
Qianying Liu (University of Glasgow),* Xiao Gu (University of Oxford), Paul Henderson (University of Glasgow), Fani Deligianni (University of Glasgow)
The 34th British Machine Vision Conference
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. Our code is available at https://github.com/kathyliu579/MCSC.Video
Citation
@inproceedings{Liu_2023_BMVC,
author = {Qianying Liu and Xiao Gu and Paul Henderson and Fani Deligianni},
title = {Multi-Scale Cross Contrastive Learning for Semi-Supervised Medical Image Segmentation},
booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023},
publisher = {BMVA},
year = {2023},
url = {https://papers.bmvc2023.org/0868.pdf}
}
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