Reci-Q: A Class-aware Patch Sampling Curriculum for Imbalanced 3D Medical Image Segmentation

Class imbalance is a fundamental challenge in medical image segmentation, where sampling strategies have been widely explored to improve the representation of underrepresented classes during training. However, sampling strategies developed for long-tailed classification cannot be directly transferred to segmentation, as dense prediction additionally requires spatial localization to determine where training crops are extracted. Existing sampling strategies for segmentation typically address this issue at a coarse foreground–background level, overlooking the substantially different prevalence of individual foreground classes. More recent adaptive and curriculum-based approaches dynamically adjust sampling during training, but often rely on auxiliary models, online difficulty signals, or additional mechanisms that increase training complexity. To address these limitations, we propose Reci-Q, a dynamic class-aware sampling strategy that explicitly decouples class selection from spatial localization. The sampling distribution is controlled by a single parameter and follows a reciprocal curriculum, transitioning from class-balanced sampling to the natural prevalence distribution and then back to class-balanced sampling. This design provides class-specific rebalancing while fully exploiting the information from prevalent samples, without any auxiliary models or explicit loss redesign. Experiments across multiple 3D medical image segmentation tasks demonstrate that Reci-Q consistently improves segmentation performance, with particularly notable gains for tail classes.