arXiv 2506.153652025-06-18eess.IVcs.CV

FedWSIDD：通过数据集蒸馏实现联邦全切片图像分类

FedWSIDD: Federated Whole Slide Image Classification via Dataset Distillation

Union Hospital, Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院附属同济医院）；Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University（澳大利亚健康创新研究所健康信息中心，麦觉大学）

Haolong Jin, Shenglin Liu, Cong Cong, Qingmin Feng, Yongzhi Liu, Lina Huang, Yingzi Hu

备注：: MICCAI 2025

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AI总结：

提出FedWSIDD，一种基于数据集蒸馏的联邦全切片图像分类方法，通过传输合成切片而非模型参数，解决异构资源与隐私问题，并在CAMELYON16/17上提升性能。

摘要：

联邦学习（FL）已成为协作医学图像分析的一种有前景的方法，使多个机构能够在保护敏感患者数据的同时构建稳健的预测模型。在全切片图像（WSI）分类的背景下，FL面临重大挑战，包括参与医疗机构的异构计算资源和隐私问题。为解决这些挑战，我们提出了FedWSIDD，一种新颖的FL范式，利用数据集蒸馏（DD）来学习并传输合成切片。在服务器端，FedWSIDD聚合来自参与中心的合成切片，并将其分发到所有中心。在客户端，我们引入了一种针对组织病理学数据集定制的新颖DD算法，该算法将染色归一化纳入蒸馏过程，以生成一组紧凑且信息量高的合成切片。这些合成切片而非模型参数被传输到服务器。通信后，接收到的合成切片与原始切片结合用于本地任务。在多个WSI分类任务（包括CAMELYON16和CAMELYON17）上的大量实验表明，FedWSIDD为异构本地模型提供了灵活性，增强了本地WSI分类性能，并保护了患者隐私。这使其成为复杂WSI分类任务的高效解决方案。代码可在FedWSIDD获取。

英文摘要：

Federated learning (FL) has emerged as a promising approach for collaborative medical image analysis, enabling multiple institutions to build robust predictive models while preserving sensitive patient data. In the context of Whole Slide Image (WSI) classification, FL faces significant challenges, including heterogeneous computational resources across participating medical institutes and privacy concerns. To address these challenges, we propose FedWSIDD, a novel FL paradigm that leverages dataset distillation (DD) to learn and transmit synthetic slides. On the server side, FedWSIDD aggregates synthetic slides from participating centres and distributes them across all centres. On the client side, we introduce a novel DD algorithm tailored to histopathology datasets which incorporates stain normalisation into the distillation process to generate a compact set of highly informative synthetic slides. These synthetic slides, rather than model parameters, are transmitted to the server. After communication, the received synthetic slides are combined with original slides for local tasks. Extensive experiments on multiple WSI classification tasks, including CAMELYON16 and CAMELYON17, demonstrate that FedWSIDD offers flexibility for heterogeneous local models, enhances local WSI classification performance, and preserves patient privacy. This makes it a highly effective solution for complex WSI classification tasks. The code is available at FedWSIDD.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。