Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/156570
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dc.contributor.authorTieu, Phat Daten_US
dc.date.accessioned2022-04-20T07:28:47Z-
dc.date.available2022-04-20T07:28:47Z-
dc.date.issued2022-
dc.identifier.citationTieu, P. D. (2022). Cross-modal synthesis of structural and functional connectome with CycleGAN for disease classification. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156570en_US
dc.identifier.urihttps://hdl.handle.net/10356/156570-
dc.description.abstractOver the last few decades, a study of human brain, or neuroscience, has grown in a significant rate. Countless researches of how our brain functions have been published. An interesting technique is to model human brain as the comprehensive map of neurons. With this perspective, it can be further studied via Structural Connectome (SC), which is a network of anatomical white matter connections in the brain, and Functional Connectome (FC), which is commonly used to assess whole brain dynamics and function. Understanding the connection between SC and FC would definitely contribute a lot to neuroscience field. This justifies the need for multi-view learning techniques to encode large datasets that combine both the brain and SC and FC. In this project, our objective is to study the cross-modal synthesis of human connectome by proposing an approach to produce SC matrices from FC matrices and vice versa, using the state-of-the-art generative model, CycleGAN. Once the synthetic samples have been created, we combine them with the orginal samples to perform multi-view disease classification, by utilizing our lab’s Convolutional Neural Network (CNN) model. This analysis aims to evaluate the improvement in classification task results when applying these simulated data.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.relationSCSE21-0426en_US
dc.subjectEngineering::Bioengineeringen_US
dc.titleCross-modal synthesis of structural and functional connectome with CycleGAN for disease classificationen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorJagath C Rajapakseen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US
dc.contributor.supervisoremailASJagath@ntu.edu.sgen_US
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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