联邦学习（FL）是医学图像分析领域的一个相对较新的研究领域，它实现了在不共享参与客户端的数据的情况下，对联邦深度学习模型进行协同学习。本文提出了一种名为FedDropoutAvg的新的联邦学习方法，用于检测结肠组织切片图像中的肿瘤。所提出的方法利用了dropout的力量，dropout是一种常用的神经网络拟合过程中避免过拟合的方法，用于客户端选择和联邦平均化过程。我们使用一个公开可用的多中心组织病理学图像数据集，针对两个不同的图像分类任务对FedDropoutAvg进行了与其他FL基准算法的比较。我们使用包含来自21个不同中心的120万个图像切片的大型数据集对所提出的模型进行训练和测试。为了测试所有模型的泛化性能，我们从未在训练中使用过的中心选择保留测试集。结果表明，所提出的方法优于其他FL方法，并且减少了FL与需要共享所有数据进行集中式训练的中心化深度学习模型之间的性能差距（在独立测试中心上，AUC的差距小于3%），这证明了所提出的FedDropoutAvg模型具有比其他最先进的联邦模型更广泛的泛化能力。据我们所知，这是第一项在组织学图像肿瘤检测的联邦设置中有效利用dropout策略的研究。© 2023年。作者，独家许可给施普林格科学+商业传媒有限责任公司、施普林格自然出版集团的一部分。

Federated learning (FL), a relatively new area of research in medical image analysis, enables collaborative learning of a federated deep learning model without sharing the data of participating clients. In this paper, we propose FedDropoutAvg, a new federated learning approach for detection of tumor in images of colon tissue slides. The proposed method leverages the power of dropout, a commonly employed scheme to avoid overfitting in neural networks, in both client selection and federated averaging processes. We examine FedDropoutAvg against other FL benchmark algorithms for two different image classification tasks using a publicly available multi-site histopathology image dataset. We train and test the proposed model on a large dataset consisting of 1.2 million image tiles from 21 different sites. For testing the generalization of all models, we select held-out test sets from sites that were not used during training. We show that the proposed approach outperforms other FL methods and reduces the performance gap (to less than 3% in terms of AUC on independent test sites) between FL and a central deep learning model that requires all data to be shared for centralized training, demonstrating the potential of the proposed FedDropoutAvg model to be more generalizable than other state-of-the-art federated models. To the best of our knowledge, ours is the first study to effectively utilize the dropout strategy in a federated setting for tumor detection in histology images.© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.