Semi-Supervised Nuclei Instance Segmentation with Category-Adaptive Sampling and Region-Adaptive Attention

Cell nuclei instance segmentation plays a critical role in pathological image analysis. In recent years, fully supervised methods for cell nuclei instance segmentation have achieved significant results. However, in practical medical image processing, annotating dense cell nuclei at the instance level is often costly and time-consuming, making it challenging to acquire large-scale labeled datasets. This challenge has motivated researchers to explore ways to further enhance segmentation performance under limited labeling conditions. To address this issue, this paper proposes a network based on category-adaptive sampling and attention mechanisms for semi-supervised nuclei instance segmentation. Specifically, we design a category-adaptive sampling method that forces the model to focus on rare categories and dynamically adapt to different data distributions. By dynamically adjusting the sampling strategy, the balance of samples across different cell types is improved. Additionally, we propose a strong–weak contrast consistency method that significantly expands the perturbation space. Strong perturbations enhance the model’s ability to discriminate key nuclei features, while weak perturbations improve its robustness against noise and interference. Furthermore, we introduce a region-adaptive attention mechanism that dynamically assigns higher weights to key regions, guiding the model to prioritize learning discriminative features in challenging areas such as blurred or ambiguous cell boundaries. This improves the morphological accuracy of the segmentation masks. Our method effectively leverages the potential information in unlabeled data, thereby reducing reliance on large-scale, high-quality labeled datasets. Experimental results on public datasets demonstrate the effectiveness of our approach.