Adaptive Learning of High-Value Regions for Semi-Supervised Medical Image Segmentation

Existing semi-supervised learning methods typically mitigate the impact of unreliable predictions by suppressing low-confidence regions. However, these methods fail to explore which regions hold higher learning value and how to design adaptive learning strategies for these regions. To address these issues, we propose a novel adaptive learning of high-value regions (ALHVR) framework. By exploiting the diversity of predictions from dual-branch networks, the prediction regions are classified into three groups: reliable stable region, reliable unstable region, and unreliable stable region. For high-value regions (reliable unstable region and unreliable stable region), different training strategies are designed. Specifically, for reliable unstable region, we propose a confidence-guided cross-prototype consistency learning (CG-CPCL) module, which enforces prototype consistency constraints in the feature space. By leveraging confidence information, the high-confidence predictions from one network selectively supervise the low-confidence predictions from the other, thus helping the model learn inter-class discrimination more stably. Additionally, for unreliable stable region, we design a dynamic teacher competition teaching (DTCT) module, which dynamically selects the most reliable pixels as teachers by evaluating the unperturbed predictions from both networks. These selected pixels are then used to supervise perturbed predictions, thereby enhancing the model's learning capability in unreliable region. Experimental results show that our method outperforms state-of-the-art approaches on three public datasets.