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Abstract #4353

A novel federated learning framework for accurate and secure multi-center MS lesion segmentation

Dongnan Liu1,2, Mariano Cabezas2, Dongang Wang2,3, Zihao Tang1,2, Geng Zhan2,3, Kain Kyle2,3, Linda Ly2,3, James Yu2,3, Chun-Chien Shieh2,3, Ryan Sullivan4, Fernando Calamante4,5, Michael Barnett2,3, Wanli Ouyang6, Weidong Cai1, and Chenyu Wang2,3
1the School of Computer Science, University of Sydney, Sydney, Australia, 2Brain and Mind Centre, University of Sydney, Sydney, Australia, 3Sydney Neuroimaging Analysis Centre, Sydney, Australia, 4the School of Biomedical Engineering, University of Sydney, Sydney, Australia, 5Sydney Imaging, University of Sydney, Sydney, Australia, 6the School of Electrical and Information Engineering, University of Sydney, Sydney, Australia

Synopsis

Keywords: Machine Learning/Artificial Intelligence, SegmentationMultiple sclerosis (MS) is a neurodegenerative disease of the central nerve system (CNS), which has the potential to cause a neurological disability, particularly for young adults. Recently, deep learning-based techniques are important for MS diagnosis and treatment, since they can segment the lesions caused by MS automatically and accurately. However, their applicability to multi-center scenarios is limited, due to the privacy and security issues in data sharing. To tackle these limitations, a decentralized deep learning framework is designed in this work, which can bring accurate multi-center MS lesion segmentation performance without sharing the raw data.

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