MMGL: Multi-Scale Multi-View Global-Local Contrastive Learning for Semi-Supervised Cardiac Image Segmentation

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MMGL: Multi-Scale Multi-View Global-Local Contrastive Learning for Semi-Supervised Cardiac Image Segmentation
Title:
MMGL: Multi-Scale Multi-View Global-Local Contrastive Learning for Semi-Supervised Cardiac Image Segmentation
Journal Title:
2022 IEEE International Conference on Image Processing (ICIP)
Publication Date:
18 October 2022
Citation:
Z. Zhao et al., & MMGL: Multi-Scale Multi-View Global-Local Contrastive Learning for Semi-Supervised Cardiac Image Segmentation, 2022 IEEE International Conference on Image Processing (ICIP), 2022, pp. 401-405, doi: 10.1109/ICIP46576.2022.9897591.
Abstract:
With large-scale well-labeled datasets, deep learning has shown significant success in medical image segmentation. However, it is challenging to acquire abundant annotations in clinical practice due to extensive expertise requirements and costly labeling efforts. Recently, contrastive learning has shown a strong capacity for visual representation learning on unlabeled data, achieving impressive performance rivaling supervised learning in many domains. In this work, we propose a novel multi-scale multi-view global-local contrastive learning (MMGL) framework to thoroughly explore global and local features from different scales and views for robust contrastive learning performance, thereby improving segmentation performance with limited annotations. Extensive experiments on the MM-WHS dataset demonstrate the effectiveness of MMGL framework on semi-supervised cardiac image segmentation, outperforming the state-of-the-art contrastive learning methods by a large margin.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done
Description:
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2381-8549
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