Semi-supervised Dual-teacher Comparative Learning with Bidirectional Bisect Copy-paste for Medical Image Segmentation
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Semi-supervised learning leverages limited pixel-level annotated data and abundant unlabeled data to achieve effective semantic image segmentation. To address this, we propose a semi-supervised learning framework, integrated with a bidirectional bisect copy-paste (B2P) mechanism. We introduce a B2CP strategy applied to labeled and unlabeled data in the second teacher network, preserving both data types to enhance training diversity. This mechanism, coupled with copy-paste-based supervision for the student network, effectively mitigates interference from uncontrollable regions. Extensive experiments on the ACDC public datasets demonstrate the efficiency of the proposed model. It surpasses the fully supervised U-Net at a 5% labeled data and 20% labeled data.
Description
@inproceedings{10.2312:pg.20251304,
booktitle = {Pacific Graphics Conference Papers, Posters, and Demos},
editor = {Christie, Marc and Han, Ping-Hsuan and Lin, Shih-Syun and Pietroni, Nico and Schneider, Teseo and Tsai, Hsin-Ruey and Wang, Yu-Shuen and Zhang, Eugene},
title = {{Semi-supervised Dual-teacher Comparative Learning with Bidirectional Bisect Copy-paste for Medical Image Segmentation}},
author = {Fang, Jiangxiong and Qi, Shikuan and Liu, Huaxiang and Fu, Youyao and Zhang, Shiqing},
year = {2025},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-295-0},
DOI = {10.2312/pg.20251304}
}