TOWARDS AN EFFICIENT SEMANTIC SEGMENTATION PIPELINE FOR 3D ELECTRON MICROSCOPY DATA.

dc.contributor.advisorCzaja, Wojciechen_US
dc.contributor.advisorGoldstein, Thomasen_US
dc.contributor.authorEmam, Zeyad Ali Samien_US
dc.contributor.departmentApplied Mathematics and Scientific Computationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2022-06-21T05:34:55Z
dc.date.available2022-06-21T05:34:55Z
dc.date.issued2022en_US
dc.description.abstractIn recent years, deep neural networks revolutionized many aspects of computer vision. However, their success relies on massive high-quality annotated datasets that are costly to curate. This thesis is composed of three major parts. In Chapter 3, we use novel high dimensional visualization methods to explore connections between the loss landscape of neural networks and their intriguing ability to generalize to unseen test data. Next, in Chapter 4, we tackle a difficult computer vision task, namely the segmentation of anisotropic 3D electron microscopy image volumes. Deep neural networks tend to struggle in this scenario due to the lack of sufficient training data and the 3 dimensional nature of the images, as such we develop a novel state-of-the-art architecture and training workflow to improve the overall segmentation pipeline. Finally, in Chapter 5 we propose a novel state-of-the-art deep active learning algorithm for image classification to alleviate the costs of data annotations and allow networks to train effectively using less data.en_US
dc.identifierhttps://doi.org/10.13016/ydm3-axst
dc.identifier.urihttp://hdl.handle.net/1903/28922
dc.language.isoenen_US
dc.subject.pqcontrolledComputer scienceen_US
dc.subject.pqcontrolledApplied mathematicsen_US
dc.subject.pquncontrolledActive Learningen_US
dc.subject.pquncontrolledDeep Learningen_US
dc.subject.pquncontrolledElectron Miscroscopyen_US
dc.subject.pquncontrolledNeural Networksen_US
dc.subject.pquncontrolledSemantic Segmentationen_US
dc.titleTOWARDS AN EFFICIENT SEMANTIC SEGMENTATION PIPELINE FOR 3D ELECTRON MICROSCOPY DATA.en_US
dc.typeDissertationen_US

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