Theses and Dissertations from UMD

Permanent URI for this communityhttp://hdl.handle.net/1903/2

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM

More information is available at Theses and Dissertations at University of Maryland Libraries.

Browse

Filters

20221

Settings

Author: search.filters.author.Emam, Zeyad Ali SamiSubject: search.filters.subject.Neural Networks

Search Results

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    TOWARDS AN EFFICIENT SEMANTIC SEGMENTATION PIPELINE FOR 3D ELECTRON MICROSCOPY DATA.
    (2022) Emam, Zeyad Ali Sami; Czaja, Wojciech; Goldstein, Thomas; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    In 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.