A cloud-based tool for federated segmentation of whole slide images

Brendon Lutnick; David Manthey; Jan U. Becker; Jonathan E. Zuckerman; Luis Rodrigues; Kuang Yu Jen; Pinaki Sarder

doi:10.1117/12.2613502

4 April 2022 A cloud-based tool for federated segmentation of whole slide images

Brendon Lutnick, David Manthey, Jan U. Becker, Jonathan E. Zuckerman, Luis Rodrigues, Kuang Yu Jen, Pinaki Sarder

Proceedings Volume 12039, Medical Imaging 2022: Digital and Computational Pathology; 120391J (2022) https://doi.org/10.1117/12.2613502
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

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.

Citation Download Citation

Brendon Lutnick, David Manthey, Jan U. Becker, Jonathan E. Zuckerman, Luis Rodrigues, Kuang Yu Jen, and Pinaki Sarder "A cloud-based tool for federated segmentation of whole slide images", Proc. SPIE 12039, Medical Imaging 2022: Digital and Computational Pathology, 120391J (4 April 2022); https://doi.org/10.1117/12.2613502

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