arXiv 2607.050352026-07-07cs.CVcs.AI

RUFNet：基于混合曼巴的查询引导支持掩码细化与不确定性融合用于少样本脑肿瘤分割

RUFNet: Query-Guided Support Mask Refinement and Uncertainty Fusion based on Hybrid Mamba for Few-Shot Brain Tumor Segmentation

Department of Language Science and Technology, The Hong Kong Polytechnic University（香港理工大学语言科学与技术系）；School of Artificial Intelligence, Chongqing University of Technology（重庆理工大学人工智能学院）；Affiliated Lianyungang Hospital of Xuzhou Medical University（徐州医科大学附属连云港医院）；College of Mechatronics and Control Engineering, Shenzhen University（深圳大学机电与控制工程学院）

Dongyi He, Xiangkai Wang, Binbing Xu, Bin Jiang, Hongjie Yan, Weixiang Liu, Wai Ting Siok, Nizhuan Wang

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AI总结：

研究针对少样本脑肿瘤分割挑战，提出基于混合曼巴的RUFNet框架，结合支持掩码细化与不确定性感知后融合，采用线性复杂度骨干等方法，在BraTS 2020数据集上表现出色。

摘要：

由于支持掩码噪声、支持与查询图像间患者差异以及缺乏像素级置信度估计，少样本脑肿瘤分割仍具挑战性。本研究提出RUFNet，一种基于混合曼巴的少样本框架，将支持掩码细化与不确定性感知后融合相结合。为以可控成本保留支持-查询依赖关系，RUFNet采用具有线性复杂度的混合曼巴交互骨干。为减少支持掩码噪声，注意力引导掩码细化模块（AGMR）使用查询特征重新校准支持掩码并提高原型一致性。为处理模糊预测，不确定性感知后融合模块（UAPF）估计像素级方差并通过查询对齐先验自适应平衡少样本预测。在脑肿瘤分割挑战（BraTS）2020数据集上，RUFNet在1路1样本和1路5样本设置下分别实现了84.3%和86.1%的骰子系数，优于比较的现有方法。这些结果表明，混合曼巴交互、掩码细化和不确定性建模可提高少样本医学图像分割的鲁棒性。官方实现代码可在此https URL获取。

英文摘要：

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.

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