Simple but Effective: Sub-Volume Contrastive Learning for Class-Imbalanced Semi-Supervised 3D Medical Image Segmentation

Medical image segmentation is essential for precise anatomical delineation and clinical decision-making. However, fully supervised methods are limited by the substantial cost of acquiring pixel-level annotations, particularly for 3D volumetric data. Semi-supervised learning (SSL) alleviates this challenge by leveraging unlabeled data, yet it remains hindered by severe class imbalance, where dominant structures disproportionately occupy the voxel space, leading to feature degradation and unreliable pseudo-labels. To address this issue, we propose a simple but effective SSL framework, namely Sub-Volume Contrastive Learning (SuVCL), to enhance feature discriminability in imbalanced 3D medical image segmentation. Our approach incorporates localized contrastive learning through sub-volume sampling, which captures small but semantically informative regions to retain fine-grained structural details while mitigating computational overhead. Furthermore, we introduce a balanced memory bank mechanism, which dynamically maintains class-specific feature representations with adaptive updates guided by class-predictive confidence. Extensive experimental evaluations demonstrate that our method substantially enhances segmentation performance for minority classes, demonstrating substantial performance gains over existing SOTAs.