Hematoxylin and Eosin (H&E) staining of whole slide images (WSIs) is considered the gold standard for pathologists and medical practitioners for tumor diagnosis, surgical planning, and post-operative assessment. With the rapid advancement of deep learning technologies, the development of numerous models based on convolutional neural networks and transformer-based models has been applied to the precise segmentation of WSIs. However, due to privacy regulations and the need to protect patient confidentiality, centralized storage and processing of image data are impractical. Training a centralized model directly is challenging to implement in medical settings due to these privacy concerns.This paper addresses the dispersed nature and privacy sensitivity of medical image data by employing a federated learning framework, allowing medical institutions to collaboratively learn while protecting patient privacy. Additionally, to address the issue of original data reconstruction through gradient inversion during the federated learning training process, differential privacy introduces noise into the model updates, preventing attackers from inferring the contributions of individual samples, thereby protecting the privacy of the training data.Experimental results show that the proposed method, FedDP, minimally impacts model accuracy while effectively safeguarding the privacy of cancer pathology image data, with only a slight decrease in Dice, Jaccard, and Acc indices by 0.55%, 0.63%, and 0.42%, respectively. This approach facilitates cross-institutional collaboration and knowledge sharing while protecting sensitive data privacy, providing a viable solution for further research and application in the medical field.

翻译：苏木精-伊红（H&E）染色的全切片图像（WSI）被病理学家和医疗从业者视为肿瘤诊断、手术规划和术后评估的金标准。随着深度学习技术的快速发展，基于卷积神经网络和Transformer架构的众多模型已被应用于WSI的精确分割。然而，由于隐私法规和保护患者机密性的要求，图像数据的集中存储与处理难以实现。基于这些隐私考量，在医疗场景中直接训练集中式模型面临实施挑战。本文针对医学图像数据的分散性和隐私敏感性，采用联邦学习框架，使医疗机构能够在保护患者隐私的前提下进行协同学习。此外，为解决联邦学习训练过程中通过梯度反演重建原始数据的问题，差分隐私技术在模型更新中引入噪声，防止攻击者推断单个样本的贡献，从而保护训练数据的隐私。实验结果表明，所提出的FedDP方法在有效保护癌症病理图像数据隐私的同时对模型精度影响极小，Dice、Jaccard和Acc指标仅分别下降0.55%、0.63%和0.42%。该方法在保护敏感数据隐私的同时促进了跨机构协作与知识共享，为医疗领域的进一步研究和应用提供了可行方案。