Unifying Active Learning and Semi-Supervised Learning for Medical Image Segmentation
Quick Answer
This paper shows that The RegAL framework unifies active and semi-supervised learning for medical image segmentation, optimizing annotation selection and unlabeled data usage.
Quick Take
It consistently outperforms state-of-the-art methods across multiple benchmarks, including BraTS 2021 and ProstateX, even with limited labeled data.
Key Points
- RegAL employs topology-aware Pareto optimization for sample acquisition and data utilization.
- It evaluates images based on voxel-wise uncertainty, feature diversity, and topological consistency.
- The framework shows stability with few labeled volumes during training.
- RegAL outperforms existing AL and SSL methods on Dice and boundary-distance metrics.
- Demonstrated effectiveness across datasets like BraTS 2021, dHCP, and ProstateX.
Paper Resources
Source Excerpt
In practical settings, medical image segmentation models are often developed with limited annotated data rather than fully labeled datasets. Training frequently begins in ultra-low labeled regimes where only a small number of volumes are annotated. In such scenarios, practitioners must simultaneously decide which cases to annotate and how to best use the remaining unlabeled data. Although active learning (AL) and semi-supervised learning (SSL) both target annotation scarcity, they are typically
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