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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