Loading...
Please wait, while we are loading the content...
Similar Documents
Meta semi-supervised medical image segmentation with label hierarchy.
| Content Provider | Europe PMC |
|---|---|
| Author | Xu, Hai Xie, Hongtao Tan, Qingfeng Zhang, Yongdong |
| Abstract | Semi-supervised learning (SSL) has attracted increasing attention in medical image segmentation, where the mainstream usually explores perturbation-based consistency as a regularization to leverage unlabelled data. However, unlike directly optimizing segmentation task objectives, consistency regularization is a compromise by incorporating invariance towards perturbations, and inevitably suffers from noise in self-predicted targets. The above issues result in a knowledge gap between supervised guidance and unsupervised regularization. To bridge the knowledge gap, this work proposes a meta-based semi-supervised segmentation framework with the exploitation of label hierarchy. Two main prominent components named Divide and Generalize, and Label Hierarchy, are built in this work. Concretely, rather than merging all knowledge indiscriminately, we dynamically divide consistency regularization from supervised guidance as different domains. Then, a domain generalization technique is introduced with a meta-based optimization objective which ensures the update on supervised guidance should generalize to the consistency regularization, thereby bridging the knowledge gap. Furthermore, to alleviate the negative impact of noise in self-predicted targets, we propose to distill the noisy pixel-level consistency by exploiting label hierarchy and extracting hierarchical consistencies. Comprehensive experiments on two public medical segmentation benchmarks demonstrate the superiority of our framework to other semi-supervised segmentation methods, with new state-of-the-art results. |
| Related Links | https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC10267083&blobtype=pdf |
| Volume Number | 11 |
| DOI | 10.1007/s13755-023-00222-1 |
| PubMed Central reference number | PMC10267083 |
| Issue Number | 1 |
| PubMed reference number | 37325196 |
| Journal | Health Information Science and Systems [Health Inf Sci Syst] |
| e-ISSN | 20472501 |
| Language | English |
| Publisher | Springer International Publishing |
| Publisher Date | 2023-06-14 |
| Publisher Place | Gewerbestrasse 11, Cham, Ch 6330, Switzerland |
| Access Restriction | Open |
| Rights License | © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| Subject Keyword | Medical image segmentation Semi-supervised learning Consistency regularization Domain generalization |
| Content Type | Text |
| Resource Type | Article |
| Subject | Health Informatics |
