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Abstract #1865

Transfer learning with progressive training as a novel approach for classifying clinical forms of multiple sclerosis based on clinical MRI

Daniel McClement1, Jinseo Lee2, Glen Pridham2, Olayinka Oladosu2, Zahra Hosseinpour2, and Yunyan Zhang2
1University of British Columbia, Vancouver, BC, Canada, 2University of Calgary, Calgary, AB, Canada

Transfer learning and greedy layer-wise training are two potential approaches to advance the performance of deep learning, particularly in fields with limited sample size including medical imaging. Taking the advantage of both, we have implemented a novel strategy that allows progressive training with transfer learning using the VGG19 network. Based on clinical MRI of 19 patients only, our approach achieved 88% accuracy in classifying relapsing remitting from secondary progressive multiple sclerosis (MS), 6% greater than training with the traditional approach. This innovative method may help provide new insight into the pathogenesis and progression mechanisms in MS.

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