Related papers: MMGL: Multi-Scale Multi-View Global-Local Contrastive learning for Semi-supervised Cardiac Image Segmentation

MMGL: Multi-Scale Multi-View Global-Local Contrastive learning for Semi-supervised Cardiac Image Segmentation

URL: http://arxiv.org/abs/2207.01883v1
Date: Tue, 5 Jul 2022 08:24:46 GMT
Title: MMGL: Multi-Scale Multi-View Global-Local Contrastive learning for Semi-supervised Cardiac Image Segmentation
Authors: Ziyuan Zhao, Jinxuan Hu, Zeng Zeng, Xulei Yang, Peisheng Qian, Bharadwaj Veeravalli, Cuntai Guan
Abstract summary: We propose a novel multi-scale multi-view global-local contrastive learning framework for medical image segmentation. Experiments on the MM-WHS dataset demonstrate the effectiveness of MMGL framework on semi-supervised cardiac image segmentation.
Score: 18.275478722238123
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.