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Abstract #2407

Successive Subspace Learning for ALS Disease Classification Using T2-weighted MRI

Xiaofeng Liu1, Fangxu Xing1, Chao Yang2, C.-C. Jay Kuo3, Suma Babu4, Georges El Fakhri1, Thomas Jenkins5, and Jonghye Woo1
1Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States, 2Facebook AI, Boston, MA, United States, 3Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States, 4Sean M Healey & AMG Center for ALS, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, BOSTON, MA, United States, 5Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, United Kingdom

A challenge in Amyotrophic Lateral Sclerosis (ALS) research and clinical practice is to detect the disease early to ensure patients have access to therapeutic trials in a timely manner. To this end, we present a successive subspace learning model for accurate classification of ALS from T2-weighted MRI. Compared with popular CNNs, our method has modular structures with fewer parameters, so is well-suited to small dataset size and 3D data. Our approach, using 20 controls and 26 patients, achieved an accuracy of 93.48% in differentiating patients from controls, which has a potential to help aid clinicians in the decision-making process.

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