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.

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Author: search.filters.author.Wang, FakaiSubject: search.filters.subject.Computer engineering

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    DEEP LEARNING APPLICATIONS IN BONE MINERAL DENSITY ESTIMATION, SPINE VERTEBRA DETECTION, AND LIVER TUMOR SEGMENTATION
    (2023) Wang, Fakai; Wu, Min; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    As the aging population and related health concerns emerge in more countries than ever, we face many challenges such as the availability, quality, and cost of medical resources. Thanks to the development of machine learning and computer vision in recent years, Deep Learning (DL) can help solve some medical problems. The diagnosis of various diseases (such as spine disorders, low bone mineral density, and liver cancer) relies on X-rays or Computed Tomography (CT). DL models could automatically analyze these radiography scans and help with the diagnosis. Different organs and diseases have distinct characteristics, requiring customized algorithms and models. In this dissertation, we investigate several Computer Aided-Diagnosis (CAD) tasks and present corresponding DL solutions. Deep Learning has multiple advantages. Firstly, DL models could uncover underlying health issues invisible to humans. One example is the opportunistic screening of Osteoporosis through chest X-ray. We develop DL models, utilizing chest film to predict bone mineral density, which helps prevent bone fractures. Humans could not tell anything about bone density in the chest film, but DL models could reliably make the prediction. The second advantage is accuracy and efficiency. Reading radiography is tedious, requiring years of expertise. This is particularly true when a radiologist needs to localize potential liver tumors by looking through tens of CT slices, spending several minutes. Deep learning models could localize and identify the tumors within seconds, greatly reducing human labor. Experiments show DL models can pick up small tumors, which are hardly seen by the naked eye. Attention should be paid to deep learning limitations. Firstly, DL models lack explainability. Deep learning models store diagnostic knowledge and statistical patterns in their parameters, which are obscure to humans. Secondly, uncertainty exists for rare diseases. If not exposed to rare cases, the models would yield uncertain outcomes. Thirdly, training AI models are subject to high-quality data but the labeling quality varies in clinical practice. Despite the challenges and issues, deep learning models are promising to promote medical diagnosis in society.
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    JOINT SPACE NARROWING CLASSIFICATION BASED ON HAND X-RAY IMAGE
    (2020) Wang, Fakai; Wu, Min; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    X-ray images have been widely used by radiologists for disease diagnosis. For Rheumatoid Arthritis (RA), Joint Space Narrowing (JSN) is one major symptom that can be read from X-ray images. In this thesis, we investigate the JSN classification for RA diagnosis in terms of methodology, data analysis, neural network models, performance analysis. First, we perform the statistical analysis of X-ray data and design a baseline convolutional neural network (CNN). We show algorithms to extract joint patches. Then we conduct prediction analysis. Second, we design the fusion model to harness the correlation between the same type of joints. Sharing information within one X-ray image would increase the prediction performance. We also compare unified classifiers and separate classifiers. Third, we design the attention map model for joints with complex contexts, which filters out unrelated surroundings. We conclude that our models give good JSN prediction for Rheumatoid Arthritis.