Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/149048
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dc.contributor.authorTang, Seanen_US
dc.date.accessioned2021-05-25T02:15:49Z-
dc.date.available2021-05-25T02:15:49Z-
dc.date.issued2021-
dc.identifier.citationTang, S. (2021). Pruning deep neural networks for encoding and decoding the human connectome. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/149048en_US
dc.identifier.urihttps://hdl.handle.net/10356/149048-
dc.description.abstractThe main focus of this project is to identify biomarkers of neurodegenerative disorders such as Alzheimer’s Disease (AD) and Parkinson’s Disease (PD) in functional Magnetic Resonance Imaging (fMRI) scans. Deep learning models can be used to encode the human functional connectome and classify between healthy subjects and patients with diseases, followed by a decoding process to identify salient features used in the classification. However, fMRI datasets have much more features than data samples, causing models to overfit easily. Existing solutions involving pruning the neural network range from recursive feature elimination which is too slow to a one-shot pruning approach which prunes too harshly. Thus, this project will explore the viability of improved pruning methodologies to attain an improved, sparser architecture. This project also goes beyond existing work on pruning multi-layer perceptron (MLP) to propose pruning approach for convolutional neural network (CNN), which can take in dynamic functional connectivity (dFC) matrices, as well as graph convolutional network (GCN), which is a better fit for encoding functional connectomes. The pruning algorithms proposed can also generalise to non-neuroimaging datasets, which is demonstrated by applying them to datasets like MNIST, CIFAR-10 and the CORA dataset, suggesting applications beyond the initial scope defined by this project.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.relationSCSE20-0229en_US
dc.subjectEngineering::Computer science and engineering::Computer applications::Life and medical sciencesen_US
dc.titlePruning deep neural networks for encoding and decoding the human connectomeen_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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