Beyond Stabilization: Dual-EMA Teachers for Global–Local Semantic Learning in Semi-Supervised Medical Image Segmentation

Semi-supervised medical image segmentation has gained increasing attention for its outstanding performance with limited annotations. Most methods follow the Mean Teacher framework, where Exponential Moving Average (EMA) is primarily used to stabilize predictions, while its potential roles beyond stabilization are largely overlooked. In this work, we interpret EMA as a first-order infinite impulse response (IIR) low-pass filter with inherent frequency selectivity, which enables the preservation of semantic information at different levels. Motivated by this insight, we propose a dual-EMA teachers framework, including a long-term teacher focusing on global structures and a short-term teacher emphasizing local details. Furthermore, to help the student better exploit the guidance from both teachers, a First-In-First-Out replay queue is designed to reuse historical high-confidence pseudo-labeled pairs, leveraging pseudo labels provided at different training stages and reducing the sensitivity to sampling bias. Meanwhile, we impose feature-level global semantic and local relation consistency constraints to alleviate the impact of noisy pseudo labels, facilitating robust transfer of complementary representations from dual teachers to the student model. Experiments on three public medical image datasets demonstrate that our method outperforms current SoTA methods, verifying its effectiveness.