Disentangling Sampling from Training Budget in Class-Imbalanced CT Body Composition Segmentation
arXiv:2605.20405v1 Announce Type: cross Abstract: 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 i
The continuous drive for more robust and reliable AI in critical applications like medical imaging pushes research into specialized areas such as handling class imbalance effectively.
Improving medical AI segmentation directly impacts diagnostic accuracy, treatment planning, and the efficiency of healthcare systems.
New methodologies for managing class imbalance in medical AI could lead to more universal and less biased diagnostic tools.
- · Medical AI development teams
- · Healthcare providers
- · Patients needing accurate diagnostics
- · Traditional segmentation methods
- · AI models without class-imbalance solutions
More accurate and reliable AI models for medical image analysis become available.
Improved medical diagnostics lead to earlier disease detection and more effective treatment protocols.
The broader adoption of AI in medical diagnosis accelerates, transforming healthcare delivery and reducing human error in interpretations.
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