RADA: Region-Aware Dual-encoder Auxiliary learning for Barely-supervised Medical Image Segmentation

• URL: http://arxiv.org/abs/2604.11164v1
• Date: Mon, 13 Apr 2026 08:26:26 GMT
• Title: RADA: Region-Aware Dual-encoder Auxiliary learning for Barely-supervised Medical Image Segmentation
• Abstract summary: Barely-supervised learning reduces annotation burden by using only a few labeled slices per volume.<n>We propose RADA, a novel Region-Aware Dual-encoder Auxiliary learning pipeline.<n>The framework combines image-level fine-grained visual features with text-level semantic guidance, providing region-aware semantic supervision.
• Score: 33.17607546116405
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 local, fine-grained visual features. Inspired by this, we propose RADA, a novel Region-Aware Dual-encoder Auxiliary learning pipeline which introduces a dual-encoder framework pre-trained on Alpha-CLIP to extract fine-grained, region-specific visual features from the original images and limited annotations. The framework combines image-level fine-grained visual features with text-level semantic guidance, providing region-aware semantic supervision that bridges image-level semantics and pixel-level segmentation. Integrated into a triple-view training framework, RADA achieves SOTA performance under extremely sparse annotation settings on LA2018, KiTS19 and LiTS, demonstrating robust generalization across diverse datasets.

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