Liu Rui's doctoral defense focused on the research topic "Application of Deep Representation Learning in Neuroimaging Analysis of Mental Disorders." During his doctoral studies, Liu Rui utilized deep learning techniques to analyze neuroimages from various perspectives, including multi-task, multi-view, and multi-modal approaches, to assist computers in diagnosing different mental disorders. His innovative achievements include: (1) Proposing a multi-task learning framework based on the overlap of symptoms and genes associated with mental illnesses. This framework enables simultaneous diagnosis of multiple mental disorders and has led to the development of a high-precision computer-aided diagnosis system. (2) Introducing a multimodal mental illness diagnosis model based on an attention mechanism, taking into account the temporal and spatial characteristics of neuroimaging. This model enables the visualization of significant regions of interest and functional connections while maintaining a high level of diagnostic accuracy, thus facilitating further exploration of the pathology of mental disorders by medical professionals. (3) Designing a specialized dynamic spatio-temporal graph model for neuroimaging using graph neural networks. This model efficiently provides dynamic representations of neural images and offers new insights for research in neuroimage analysis.

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Liu Rui received his B.S. degree in intelligence science and technology from the Chongqing University of Posts and Telecommunications, Chongqing, China, in 2019. His current research interests include machine learning, multi-task/modality learning, neuroimaging analysis, medical images diagnosis, and applied deep learning. He has published four papers in prestigious research journals, as the first or corresponding author, including IEEE Transactions on Neural Networks and Learning Systems (IF: 10.4) and Computers in Biology and Medicine (IF: 7.7). Additionally, Liu Rui has applied for two national invention patents.

 

Dr. Liu will join The Hong Kong Polytechnic University as a postdoctoral fellow, where he will continue his research in medical image diagnosis and the application of deep learning. Wishing him to have a fruitful and fulfilling postdoc journey ahead!

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