Lutnick Brendon, Manthey David, Becker Jan U, Zuckerman Jonathan E, Rodrigues Luis, Jen Kuang Yu, Sarder Pinaki
Department of Pathology and Anatomical Sciences, SUNY Buffalo, NY.
Kitware Incorporated, Clifton Park, NY.
Proc SPIE Int Soc Opt Eng. 2022 Feb-Mar;12039. doi: 10.1117/12.2613502. Epub 2022 Apr 4.
It is commonly known that diverse datasets of WSIs are beneficial when training convolutional neural networks, however sharing medical data between institutions is often hindered by regulatory concerns. We have developed a cloud-based tool for federated WSI segmentation, allowing collaboration between institutions without the need to directly share data. To show the feasibility of federated learning on pathology data in the real world, We demonstrate this tool by segmenting IFTA from three institutions and show that keeping the three datasets separate does not hinder segmentation performance. This pipeline is deployed in the cloud for easy access for data viewing and annotation by each site's respective constituents.
众所周知，在训练卷积神经网络时，多样的全切片图像（WSI）数据集是有益的，然而机构之间共享医学数据往往受到监管问题的阻碍。我们开发了一种用于联合WSI分割的基于云的工具，允许机构之间进行协作而无需直接共享数据。为了展示联合学习在现实世界中病理学数据上的可行性，我们通过对来自三个机构的免疫荧光-肾小管间质纤维化区域（IFTA）进行分割来演示此工具，并表明保持这三个数据集分开不会妨碍分割性能。该流程部署在云端，以便每个站点的相关人员能够轻松访问数据进行查看和标注。
