Abstract #3996

A Novel Multi-Center Classification Method for ASD Diagnosis via Sparse Multi-Modality Multi-Task Learning

Jun Wang^1,2, Qian Wang³, Jialin Peng¹, Dong Nie¹, Feng Zhao¹, Chong-Yaw Wee⁴, Shitong Wang², and Dinggang Shen¹

¹Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, ²School of Digital Media, Jiangnan University, Wuxi, People's Republic of China, ³Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China, ⁴Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore

Multi-task classification targeting multi-center ASD diagnosis is not well investigated yet. Taking advantages of the Autism Brain Imaging Data Exchange (ABIDE) database, we propose a novel multi-modality multi-center classification (M3CC) method for accurate ASD diagnosis. We formulate the diagnosis into a multi-task learning problem, as each task corresponds to the classification of the subjects of one center. Our comprehensive experiments show that, by incorporating multi-modality neuroimaging data and handling multiple centers jointly, the performance of computer-assisted ASD diagnosis is increased significantly.

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