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The student-teacher framework guided by self-training and consistency regularization for semi-supervised medical image segmentation.
- Source :
- PLoS ONE; 4/22/2024, Vol. 19 Issue 4, p1-18, 18p
- Publication Year :
- 2024
-
Abstract
- Due to the high suitability of semi-supervised learning for medical image segmentation, a plethora of valuable research has been conducted and has achieved noteworthy success in this field. However, many approaches tend to confine their focus to a singular semi-supervised framework, thereby overlooking the potential enhancements in segmentation performance offered by integrating several frameworks. In this paper, we propose a novel semi-supervised framework named Pesudo-Label Mean Teacher (PLMT), which synergizes the self-training pipeline with pseudo-labeling and consistency regularization techniques. In particular, we integrate the student-teacher structure with consistency loss into the self-training pipeline to facilitate a mutually beneficial enhancement between the two methods. This structure not only generates remarkably accurate pseudo-labels for the self-training pipeline but also furnishes additional pseudo-label supervision for the student-teacher framework. Moreover, to explore the impact of different semi-supervised losses on the segmentation performance of the PLMT framework, we introduce adaptive loss weights. The PLMT could dynamically adjust the weights of different semi-supervised losses during the training process. Extension experiments on three public datasets demonstrate that our framework achieves the best performance and outperforms the other five semi-supervised methods. The PLMT is an initial exploration of the framework that melds the self-training pipeline with consistency regularization and offers a comparatively innovative perspective in semi-supervised image segmentation. [ABSTRACT FROM AUTHOR]
- Subjects :
- SUPERVISED learning
DIAGNOSTIC imaging
IMAGE segmentation
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 19
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- PLoS ONE
- Publication Type :
- Academic Journal
- Accession number :
- 176761261
- Full Text :
- https://doi.org/10.1371/journal.pone.0300039