Synergy-Guided Regional Supervision of Pseudo Labels for Semi-Supervised Medical Image Segmentation
This work addresses a domain-specific problem in medical image segmentation by improving semi-supervised learning, though it appears incremental as it builds on existing mean teacher networks.
The paper tackled noise contamination in pseudo labeling for semi-supervised medical image segmentation by introducing the SGRS-Net framework, which uses synergy-guided regional supervision and demonstrated superior performance on the LA dataset.
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.