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dc.contributor.authorPhan, Tran Hong Ha
dc.date.accessioned2019-06-11
dc.date.available2019-06-11
dc.date.issued2019-01-30
dc.identifier.urihttp://hdl.handle.net/2123/20530
dc.description.abstractCell images are essential in modern medicine and biological research, such as in cancer diagnosis and drug development. A major obstacle for the application of traditional machine learning algorithms is caused by the large variability of the spatio-temporal patterns in cell images. The shape, size, and motion patterns of the same family of cells can have vastly different visual appearance according to the physical and biological setting in which the images were acquired. This thesis addresses the gaps between machine learning methods and their practical applications in cell image analysis by developing new methods that use neural networks to learn to characterize subtle spatio-temporal patterns in cell images in order to minimize reliance upon human inputs and to maximize generalizability. To achieve this goal, we introduce four methods for cell analysis: (1) an unsupervised method that learns the distinctions between different components of the spatio-temporal patterns of cells to detect and classify cell events in time-lapse phase-contrast microscopy (PCM) videos; (2) a semi-supervised method that exploits the spatio-temporal patterns of cells in their normal stage in PCM videos and performs unsupervised estimation of temporal lengths of cell events; (3) a supervised method that enables end-to-end learning and prediction of contextual spatio-temporal patterns in PCM videos; and (4) our transfer learning framework that extracts useful spatial features for cells. We conducted experiments on a public datasets, which have been used extensively in previous studies and competitions. It comprises of visual data of cells with different cell types, various cell shapes and sizes, irregular cell motion patterns, and densely packed cell populations. Our results show that our methods are more accurate, data efficient, and generalizable than other state-of-the-art methods.en_AU
dc.publisherUniversity of Sydneyen_AU
dc.publisherFaculty of Engineering and ITen_AU
dc.publisherSchool of Computer Scienceen_AU
dc.rightsThe author retains copyright of this thesis. It may only be used for the purposes of research and study. It must not be used for any other purposes and may not be transmitted or shared with others without prior permission.en_AU
dc.subjectDeepen_AU
dc.subjectLearningen_AU
dc.subjectCellen_AU
dc.subjectEventen_AU
dc.subjectDetectionen_AU
dc.titleNeural network approaches for cell and event detection in biomedical microscopy imaging using spatial and temporal patternsen_AU
dc.typePhD Doctorateen_AU
dc.type.pubtypeDoctor of Philosophy Ph.D.en_AU
dc.description.disclaimerAccess is restricted to staff and students of the University of Sydney . UniKey credentials are required. Non university access may be obtained by visiting the University of Sydney Library.en_AU


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