Inherent Consistent Learning for Accurate Semi-supervised Medical Image Segmentation
This addresses the high cost of medical image annotations for segmentation, though it appears incremental as it builds on existing semi-supervised learning approaches.
The paper tackles the problem of semi-supervised medical image segmentation by proposing Inherent Consistent Learning (ICL), which uses semantic consistency guidance to learn robust representations from labeled and unlabeled data. Experimental results on three public benchmarks show that ICL outperforms state-of-the-art methods, particularly when annotated data is extremely limited.
Semi-supervised medical image segmentation has attracted much attention in recent years because of the high cost of medical image annotations. In this paper, we propose a novel Inherent Consistent Learning (ICL) method, aims to learn robust semantic category representations through the semantic consistency guidance of labeled and unlabeled data to help segmentation. In practice, we introduce two external modules, namely Supervised Semantic Proxy Adaptor (SSPA) and Unsupervised Semantic Consistent Learner (USCL) that is based on the attention mechanism to align the semantic category representations of labeled and unlabeled data, as well as update the global semantic representations over the entire training set. The proposed ICL is a plug-and-play scheme for various network architectures, and the two modules are not involved in the testing stage. Experimental results on three public benchmarks show that the proposed method can outperform the state-of-the-art, especially when the number of annotated data is extremely limited. Code is available at: https://github.com/zhuye98/ICL.git.