Disentangling Sampling from Training Budget in Class-Imbalanced CT Body Composition Segmentation
Class imbalance is a fundamental challenge in medical image segmentation, where frequent classes typically dominate training at the expense of rare classes. Loss-based approaches mitigate imbalance by reweighting the per-pixel loss within the batch, while sampling strategies control which images enter the batch. Yet neither explicitly controls which classes appear within the batch, leaving rare-class exposure only partially rebalanced. In this work, we adopt episodic sampling from few-shot learning to promote class-balanced batch construction in a fully supervised setting. We decouple episodic sampling from its conventional metric-learning context and evaluate it in body composition segme...
| Authors | Iason Skylitsis, Dimitrios Karkalousos, Ivana Išgum |
|---|---|
| Primary category | eess.IV |
| Published | 19 May 2026 |
| Last updated at source | 19 May 2026 |
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