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Abstract #3996

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

Jun Wang1,2, 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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