Boosting Semi-Supervised Medical Image Segmentation via Masked Image Consistency and Discrepancy Learning
This work addresses the challenge of leveraging unlabeled data for medical image segmentation, which is crucial for reducing annotation costs in healthcare, but it appears incremental as it builds on existing co-training strategies.
The paper tackles the problem of improving semi-supervised medical image segmentation by addressing the balance between information exchange and model diversity in co-training frameworks, resulting in state-of-the-art performance on AMOS and Synapse datasets.
Semi-supervised learning is of great significance in medical image segmentation by exploiting unlabeled data. Among its strategies, the co-training framework is prominent. However, previous co-training studies predominantly concentrate on network initialization variances and pseudo-label generation, while overlooking the equilibrium between information interchange and model diversity preservation. In this paper, we propose the Masked Image Consistency and Discrepancy Learning (MICD) framework with three key modules. The Masked Cross Pseudo Consistency (MCPC) module enriches context perception and small sample learning via pseudo-labeling across masked-input branches. The Cross Feature Consistency (CFC) module fortifies information exchange and model robustness by ensuring decoder feature consistency. The Cross Model Discrepancy (CMD) module utilizes EMA teacher networks to oversee outputs and preserve branch diversity. Together, these modules address existing limitations by focusing on fine-grained local information and maintaining diversity in a heterogeneous framework. Experiments on two public medical image datasets, AMOS and Synapse, demonstrate that our approach outperforms state-of-the-art methods.