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

arXiv: https://arxiv.org/abs/2604.11164 · HTML: https://mlsanity.com/paper/2604.11164/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 13 Apr 2026.

## Builds on (most cited first, 7 of 7)

- [The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes](https://mlsanity.com/paper/1904.00445/) (2019)
- [PixCLIP: Achieving Fine-grained Visual Language Understanding via Any-granularity Pixel-Text Alignment Learning](https://mlsanity.com/paper/2511.04601/) (2025)
- [SGTC: Semantic-Guided Triplet Co-training for Sparsely Annotated Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2412.15526/) (2024)
- [SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training](https://mlsanity.com/paper/2504.14737/) (2025)
- [Multi-level Asymmetric Contrastive Learning for Volumetric Medical Image Segmentation Pre-training](https://mlsanity.com/paper/2309.11876/) (2023)
- [Novel Extraction of Discriminative Fine-Grained Feature to Improve Retinal Vessel Segmentation](https://mlsanity.com/paper/2505.03896/) (2025)
- [Improve Retinal Artery/Vein Classification via Channel Couplin](https://mlsanity.com/paper/2508.03738/) (2025)

## Abstract (arXiv)

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.