English(EN) Federated Multi-Task Learning for Bladder Tumor Segmentation and MIBC Classification Using a Hybrid CNN-Transformer Architecture

联邦学习框架改进膀胱癌分割和分类

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了一个联邦多任务学习框架，利用T2加权MRI改进膀胱肿瘤分割和肌层浸润性膀胱癌（MIBC）分类。提出的Swin Hybrid模型结合了ResNet-34和Swin-Tiny Transformer分支，旨在克服机构间数据隐私和成像变异性带来的挑战。在FedBCa数据集上的实验表明，Swin Hybrid架构，特别是在联邦训练下使用Geo+Elastic增强时，分割的Dice相似系数（DSC）达到0.8100，分类的患者级别曲线下面积（AUC）达到0.8931。 AI

影响该研究展示了一种在不损害患者数据隐私的情况下，跨机构进行协作医学图像分析的可行方法。

排序理由该集群包含一篇详细介绍新机器学习架构和实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Malhar Udmale, Divyanshu Dwivedi, Aarohi Dhand, Sachin Dudda Nagaraju, Mayank Rai, Bagesh Kumar · 2026-09-01 04:00

基于混合CNN-Transformer架构的用于膀胱肿瘤分割和MIBC分类的联邦多任务学习

arXiv:2608.30458v1 Announce Type: new Abstract: Accurate bladder tumor segmentation and assessment of mus- cle invasion from T2-weighted MRI are important for treatment plan- ning, but developing robust models across institutions is challenging be- cause patient data cannot be ce…

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基于混合CNN-Transformer架构的用于膀胱肿瘤分割和MIBC分类的联邦多任务学习

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