Constrain Latent Space for Schizophrenia Classification via Dual Space Mapping Net
文献类型:会议论文
作者 | Weiyang, Shi5,6; Kaibin, Xu6; Ming, Song5,6; Lingzhong, Fan3,5,6; Tianzi, Jiang1,2,3,4,5,6 |
出版日期 | 2020 |
会议日期 | 2020-10 |
会议地点 | Peru |
关键词 | Schizophrenia Clinical symptoms Multi-view learning |
英文摘要 | Mining potential biomarkers of schizophrenia (SCZ) while performing classification is essential for the research of SCZ. However, most related studies only perform a simple binary classification with high-dimensional neuroimaging features that ignore individual’s unique clinical symptoms. And the biomarkers mined in this way are more susceptible to confounding factors such as demographic factors. To address these questions, we propose a novel end-to-end framework, named Dual Spaces Mapping Net (DSM-Net), to map the neuroimaging features and clinical symptoms to a shared decoupled latent space, so that constrain the latent space into a solution space associated with detailed symptoms of SCZ. Briefly, taking functional connectivity patterns and the Positive and Negative Syndrome Scale (PANSS) scores as input views, DSM Net maps the inputs to a shared decoupled latent space which is more discriminative. Besides, with an invertible space mapping sub-network, DSM-Net transforms multi-view learning into multi-task learning and provides regression of PANSS scores as an extra benefit. We evaluate the proposed DSM-Net with multi-site data of SCZ in the leave-one-site-out cross validation setting and experimental results illustrate the effectiveness of DSM-Net in classification, regression performance, and unearthing neuroimaging biomarkers with individual specificity, population commonality and less effect of confusions. |
语种 | 英语 |
源URL | [http://ir.ia.ac.cn/handle/173211/50616] |
专题 | 自动化研究所_脑网络组研究中心 |
通讯作者 | Tianzi, Jiang |
作者单位 | 1.Queensland Brain Institute, University of Queensland, Brisbane, Australia 2.Key Laboratory for Neuro Information of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China 3.Innovation Academy for Artificial Intelligence, Chinese Academy of Sciences, Beijing, China 4.Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China 5.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China 6.Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China |
推荐引用方式 GB/T 7714 | Weiyang, Shi,Kaibin, Xu,Ming, Song,et al. Constrain Latent Space for Schizophrenia Classification via Dual Space Mapping Net[C]. 见:. Peru. 2020-10. |
入库方式: OAI收割
来源:自动化研究所
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