RUFNet: Query-Guided Support Mask Refinement and Uncertainty Fusion based on Hybrid Mamba for Few-Shot Brain Tumor Segmentation
2026-07-06 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionArtificial Intelligence
AI summaryⓘ
The authors developed RUFNet, a method to help computers better identify brain tumors in medical images using very few examples. Their approach improves on previous ones by cleaning up support masks using attention-based refinement and by estimating uncertainty to make smarter predictions. They designed a special backbone called Hybrid Mamba to efficiently handle interactions between example and target images. Tested on a brain tumor dataset, their method performed better than others in segmenting tumors accurately with limited data.
few-shot learningbrain tumor segmentationsupport mask refinementuncertainty estimationHybrid Mambaattention mechanismposterior fusionDice coefficientmedical image segmentationBraTS dataset
Authors
Dongyi He, Xiangkai Wang, Binbing Xu, Bin Jiang, Hongjie Yan, Weixiang Liu, Wai Ting Siok, Nizhuan Wang
Abstract
Few-shot brain tumor segmentation remains challenging due to noisy support masks, inter-patient variations between support and query images, and the lack of pixel-wise confidence estimation. This study proposes RUFNet, a Hybrid Mamba-based few-shot framework that combines support mask refinement with uncertainty-aware posterior fusion. To preserve support-query dependencies with manageable cost, RUFNet adopts a Hybrid Mamba interaction backbone with linear complexity. To reduce support-mask noise, an Attention-Guided Mask Refinement module (AGMR) uses query features to recalibrate support masks and improve prototype consistency. To handle ambiguous predictions, an Uncertainty-Aware Posterior Fusion module (UAPF) estimates pixel-wise variance and adaptively balances few-shot predictions with query-aligned priors. On the Brain Tumor Segmentation Challenge (BraTS) 2020 dataset, RUFNet achieves Dice coefficients of 84.3% and 86.1% in the 1-way 1-shot and 1-way 5-shot settings, respectively, outperforming the compared state-of-the-art methods. These results suggest that Hybrid Mamba interaction, mask refinement and uncertainty modelling can improve the robustness of few-shot medical image segmentation. The official implementation code is available at https://github.com/hdy6438/RUFNet.