Deep Learning-based computational pathology algorithms have demonstrated profound ability to excel in a wide array of tasks that range from characterization of well known morphological phenotypes to predicting non human-identifiable features from histology such as molecular alterations. However, the development of robust, adaptable and accurate deep learning-based models often rely on the collection and time-costly curation large high-quality annotated training data that should ideally come from diverse sources and patient populations to cater for the heterogeneity that exists in such datasets. Multi-centric and collaborative integration of medical data across multiple institutions can naturally help overcome this challenge and boost the model performance but is limited by privacy concerns among other difficulties that may arise in the complex data sharing process as models scale towards using hundreds of thousands of gigapixel whole slide images. In this paper, we introduce privacy-preserving federated learning for gigapixel whole slide images in computational pathology using weakly-supervised attention multiple instance learning and differential privacy. We evaluated our approach on two different diagnostic problems using thousands of histology whole slide images with only slide-level labels. Additionally, we present a weakly-supervised learning framework for survival prediction and patient stratification from whole slide images and demonstrate its effectiveness in a federated setting. Our results show that using federated learning, we can effectively develop accurate weakly-supervised deep learning models from distributed data silos without direct data sharing and its associated complexities, while also preserving differential privacy using randomized noise generation. We also make available an easy-to-use federated learning for computational pathology software package: http://github.com/mahmoodlab/HistoFL.

基于深度学习的计算病理学算法已经证明了在各种任务中表现出色的强大能力，这些任务包括从众所周知的形态学表型的表征到从组织学中预测非人类可识别的特征（例如分子改变）。然而，稳健、适应性强且准确的基于深度学习的模型的开发通常依赖于大量高质量带注释训练数据的收集和耗时的管理，这些数据最好来自不同的来源和患者群体，以适应临床中存在的异质性。这样的数据集。跨多个机构的医疗数据的多中心和协作集成自然可以帮助克服这一挑战并提高模型性能，但受到隐私问题以及随着模型扩展到使用数十万个数据而在复杂的数据共享过程中可能出现的其他困难的限制。十亿像素的整个幻灯片图像。在本文中，我们使用弱监督注意多实例学习和差分隐私，介绍了计算病理学中十亿像素整个幻灯片图像的隐私保护联合学习。我们使用数千个仅带有载玻片级标签的组织学完整载玻片图像评估了我们针对两个不同诊断问题的方法。此外，我们提出了一个弱监督学习框架，用于从整个幻灯片图像进行生存预测和患者分层，并证明其在联合环境中的有效性。我们的结果表明，使用联邦学习，我们可以有效地从分布式数据孤岛开发准确的弱监督深度学习模型，而无需直接数据共享及其相关的复杂性，同时还可以使用随机噪声生成来保护差异隐私。我们还提供了易于使用的联合学习计算病理学软件包：http://github.com/mahmoodlab/HistoFL。