RADA: Region-Aware Dual-encoder Auxiliary learning for Barely-supervised Medical Image Segmentation
Deep learning has greatly advanced medical image segmentation, but its success relies heavily on fully supervised learning, which requires dense annotations that are costly and time-consuming for 3D volumetric scans. Barely-supervised learning reduces annotation burden by using only a few labeled slices per volume. Existing methods typically propagate sparse annotations to unlabeled slices through geometric continuity to generate pseudo-labels, but this strategy lacks semantic understanding, often resulting in low-quality pseudo-labels. Furthermore, medical image segmentation is inherently a pixel-level visual understanding task, where accuracy fundamentally depends on the quality of loca...
| Authors | Shuang Zeng, Boxu Xie, Lei Zhu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 13 April 2026 |
| Last updated at source | 13 April 2026 |
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