Fed-ADApt：面向资源感知医学图像分割的联邦任意深度自适应
Fed-ADApt: Federated Anytime Depth Adaptation for Resource-Aware Medical Image Segmentation
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中文总结 AI 辅助
提出Fed-ADApt，一种深度自适应的联邦学习框架，使低资源机构能以降低的训练和推理成本参与医学图像分割，同时保持与全资源FedAvg相当的全局性能。
中文摘要 AI 辅助
联邦学习（FL）能够在无需共享原始患者数据的情况下协作训练医学图像分割模型，然而现有方法假设各机构具有同质的计算预算，限制了低资源站点的参与。我们提出Fed-ADApt，一种面向基于UNet的分割的深度自适应联邦框架，同时解决低计算训练和推理问题。Fed-ADApt集成了多深度监督与分层深度聚合，允许每个站点根据其本地计算预算进行训练，同时为支持部署时动态深度选择的全局模型做出贡献。我们在多站点2D视网膜眼底视盘分割和3D脑肿瘤分割上评估了Fed-ADApt。在这两项任务中，联邦协作显著提高了域偏移下的鲁棒性。Fed-ADApt在3D任务中匹配了全资源FedAvg性能，在2D任务中取得了具有竞争力的性能，平均Dice降低4.7%，同时将3D平均推理成本降低19.5%，2D降低34.5%，并在最受限站点将训练成本大幅降低98%。重要的是，Fed-ADApt使无法训练全容量模型的低资源机构能够参与联邦，同时在有利的精度-效率权衡下保持具有竞争力的全局性能。通过考虑训练和推理计算预算，Fed-ADApt为跨异构临床和边缘成像环境的联邦医学图像分割提供了一种实用且公平的解决方案。
英文摘要
Federated learning (FL) enables collaborative training of medical image segmentation models without sharing raw patient data, yet existing approaches assume a homogeneous compute budget across institutions, limiting participation of low-resource sites. We propose Fed-ADApt, a depth-adaptive federated framework for UNet-based segmentation that jointly addresses low-compute training and inference. Fed-ADApt integrates multi-depth supervision with hierarchical depth-wise aggregation, allowing each site to train according to its local compute budget while contributing to a global model that supports dynamic depth selection at deployment. We evaluated Fed-ADApt on multi-site 2D retinal fundus disc segmentation and 3D brain tumor segmentation. Across both tasks, federated collaboration substantially improves robustness under domain shift. Fed-ADApt matched the full-resource FedAvg performance in 3D and achieved competitive 2D performance with a 4.7% average Dice reduction, while reducing average inference cost by 19.5% in 3D and 34.5% in 2D and substantially reducing training cost by 98% at the most constrained sites. Importantly, Fed-ADApt enables low-resource institutions that cannot train full-capacity models to participate in federations while maintaining competitive global performance under a favorable accuracy to efficiency trade-off. By considering training and inference compute budgets, Fed-ADApt provides a practical and equitable solution for federated medical image segmentation across heterogeneous clinical and edge-enabled imaging environments.
发表机构
- Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid（马德里理工大学电信工程学院）
- CIBER-BBN, Instituto de Salud Carlos III（卡洛斯三世健康研究所生物工程、生物材料与纳米医学网络中心）
- Departments of Radiology and Pediatrics, The George Washington University（乔治华盛顿大学放射学与儿科学系）
- NVIDIA Corporation（英伟达公司）
机构由 AI 辅助整理，请以论文原文为准。
