Abstract #4353

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

Dongnan Liu^1,2, Mariano Cabezas², Dongang Wang^2,3, Zihao Tang^1,2, Geng Zhan^2,3, Kain Kyle^2,3, Linda Ly^2,3, James Yu^2,3, Chun-Chien Shieh^2,3, Ryan Sullivan⁴, Fernando Calamante^4,5, Michael Barnett^2,3, Wanli Ouyang⁶, Weidong Cai¹, and Chenyu Wang^2,3

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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