Semi-supervised learning has received considerable attention for its potential to leverage abundant unlabeled data to enhance model robustness. Pseudo labeling is a widely used strategy in semi supervised learning. However, existing methods often suffer from noise contamination, which can undermine model performance. To tackle this challenge, we introduce a novel Synergy-Guided Regional Supervision of Pseudo Labels (SGRS-Net) framework. Built upon the mean teacher network, we employ a Mix Augmentation module to enhance the unlabeled data. By evaluating the synergy before and after augmentation, we strategically partition the pseudo labels into distinct regions. Additionally, we introduce a Region Loss Evaluation module to assess the loss across each delineated area. Extensive experiments conducted on the LA dataset have demonstrated superior performance over state-of-the-art techniques, underscoring the efficiency and practicality of our framework.
@article{arxiv.2411.04493,
title = {Synergy-Guided Regional Supervision of Pseudo Labels for Semi-Supervised Medical Image Segmentation},
author = {Tao Wang and Xinlin Zhang and Yuanbin Chen and Yuanbo Zhou and Longxuan Zhao and Tao Tan and Tong Tong},
journal= {arXiv preprint arXiv:2411.04493},
year = {2024}
}