[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84786-en":3,"doc-seo-84786-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},84786,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","RUFNet: Query-Guided Support Mask Refinement and Uncertainty Fusion based on Hybrid Mamba for Few-shot Brain Tumor Segmentation","Few-shot brain tumor segmentation faces three key issues: noisy support masks, inter-patient appearance discrepancies between support and query images, and missing pixel-wise confidence estimation. RUFNet introduces a Hybrid Mamba-based few-shot framework that integrates attention-guided mask refinement with uncertainty-aware posterior fusion. A Hybrid Mamba interaction backbone preserves support-query dependencies at linear cost. Experiments on BraTS 2020 yield Dice of 84.3% (1-way 1-shot) and 86.1% (1-way 5-shot), outperforming prior work.","arXiv :2607 .05035v 1 [ cs .CV] 6 Jul 2026  \nRUFNET: QUERY-GUIDED SUPPORT MASK REFINEMENT AND UNCERTAINTY FUSION BASED ON HYBRID MAMBA FOR FEW-SHOT BRAIN TUMOR SEGMENTATION  \nDongyi He 1 , Xiangkai Wang2 , Binbing Xu2 , Bin Jiang2 , Hongjie Yan3 , Weixiang Liu4 , Wai Ting Siok1 , and Nizhuan  \nWang 1,*  \n1Department of Language Science and Technology, The Hong Kong Polytechnic University, Hong Kong SAR, China  \n2 School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China  \n3Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, China  \n4 College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China  \n* [Correspondence: wangnizhuan1120@gmail.com](Correspondence: wangnizhuan1120@gmail.com)  \nABSTRACT  \nFew-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.  \nThese 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](https://github.com/hdy6438/RUFNet).  \nKeywords Few-shot learning · Brain tumor segmentation · Mask refinement · Uncertainty estimation · Mamba  \n1 Introduction  \nBrain tumor segmentation in multi-modal magnetic resonance imaging (MRI) is a fundamental task in neuro-oncology image analysis. Accurate delineation of tumor regions and subregions supports diagnosis, treatment planning and longitudinal assessment, but manual annotation is time-consuming and depends on specialist expertise. Deep learning has therefore become an important approach for automated brain tumor segmentation [1] .  \nDespite some progress, high-performing segmentation networks remain annotation-intensive. nnU-Net adapts preprocessing and architecture to each dataset, but still assumes enough labelled cases for reliable configuration and training [2] . Swin-UNet introduces Transformer-style long-range modelling, but its attention-based design is data hungry and computationally heavier when dense image interactions are required [3] . These requirements are difficult to satisfy in clinical settings, where rare tumor subtypes, small patient cohorts and expensive voxel-level annotation limit labelled data. Few-shot learning can reduce this burden, but brain tumor models remain vulnerable because tumor appearance, size and location vary substantially across patients [4] .  \nExisting few-shot segmentation methods address parts of this problem, but leave important gaps. PANet uses prototype alignment, yet its prototypes can be corrupted by inaccurate support masks [5] . SENet improves channel recalibration,  \nRUFNet  \nFigure 1: Overall architecture of RUFNet. Support and query images are encoded by a shared backbone, the support mask is refined by AGMR, and Hybrid Mamba blocks model support-query interaction. UAPF then fuses the metaprediction with the refined prior to produce the final segmentation.  \nbut does not expl","cbCaikwudWvFTQRk","https://ap.wps.com/l/cbCaikwudWvFTQRk","pdf",2579333,2,1,9,"English","en",105,"# Abstract\n# Introduction\n## Background and motivation\n## Limitations of existing few-shot methods\n## Design rationale and challenges","[{\"question\":\"What problems does RUFNet target in few-shot brain tumor segmentation?\",\"answer\":\"It addresses noisy support masks, appearance variations between support and query images across patients, and the absence of pixel-wise confidence estimation for uncertain boundaries.\"},{\"question\":\"How does RUFNet refine support masks?\",\"answer\":\"RUFNet uses an Attention-Guided Mask Refinement (AGMR) module that recalibrates support masks using query features to improve prototype consistency.\"},{\"question\":\"How does RUFNet handle uncertainty during prediction fusion?\",\"answer\":\"The Uncertainty-Aware Posterior Fusion (UAPF) module estimates pixel-wise variance and adaptively balances few-shot predictions with query-aligned 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problems does RUFNet target in few-shot brain tumor segmentation?","Question",{"text":75,"@type":76},"It addresses noisy support masks, appearance variations between support and query images across patients, and the absence of pixel-wise confidence estimation for uncertain boundaries.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does RUFNet refine support masks?",{"text":80,"@type":76},"RUFNet uses an Attention-Guided Mask Refinement (AGMR) module that recalibrates support masks using query features to improve prototype consistency.",{"name":82,"@type":73,"acceptedAnswer":83},"How does RUFNet handle uncertainty during prediction fusion?",{"text":84,"@type":76},"The Uncertainty-Aware Posterior Fusion (UAPF) module estimates pixel-wise variance and adaptively balances few-shot predictions with query-aligned 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