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Medicine and Health Sciences Commons

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Analytical, Diagnostic and Therapeutic Techniques and Equipment

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Theses/Dissertations

2021

Deep learning

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Full-Text Articles in Medicine and Health Sciences

Developing Deep-Learning Methods For Diagnosis And Prognosis Of Pediatric Progressive Diseases Using Modern Imaging Techniques, Mahdieh Shabanian Dec 2021

Developing Deep-Learning Methods For Diagnosis And Prognosis Of Pediatric Progressive Diseases Using Modern Imaging Techniques, Mahdieh Shabanian

Theses and Dissertations (ETD)

Purpose and Rationale. Central nervous system manifestations form a significant burden of disease in young children. There have been efforts to correlate the neurological disease state in tuberous sclerosis complex (TSC) neurological disease state with imaging findings is a standard part of patient care. However, such analysis of neuroimaging is time- and labor-intensive. Automated approaches to these tasks are needed to improve speed, accuracy, and availability. Automated medical image analysis tools based on 3D/2D deep learning algorithms can help improve the quality and consistency of image diagnosis and interpretation for cognitive disorders in infants. We propose to automate neuroimaging analysis …


Advancing Proper Dataset Partitioning And Classification Of Visual Search And The Vigilance Decrement Using Eeg Deep Learning Algorithms, Alexander J. Kamrud Sep 2021

Advancing Proper Dataset Partitioning And Classification Of Visual Search And The Vigilance Decrement Using Eeg Deep Learning Algorithms, Alexander J. Kamrud

Theses and Dissertations

Electroencephalography (EEG) classification of visual search and vigilance tasks has vast potential in its benefits. In future human-machine teaming systems, EEG could act as the tool for operator state assessment, enabling AI teammates to know when to assist the operator in these tasks, with the potential to lead to increased safety of operations, better training systems for our operators, and improved operational effectiveness. This research investigates deep learning methods which utilize EEG signals to classify the efficiency of an operator's search and to classify whether an operator is in a decrement during a vigilance type task, and investigates performing these …