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An Explainable Deep Learning Prediction Model For Severity Of Alzheimer's Disease From Brain Images, Godwin O. Ekuma
An Explainable Deep Learning Prediction Model For Severity Of Alzheimer's Disease From Brain Images, Godwin O. Ekuma
MSU Graduate Theses
Deep Convolutional Neural Networks (CNNs) have become the go-to method for medical imaging classification on various imaging modalities for binary and multiclass problems. Deep CNNs extract spatial features from image data hierarchically, with deeper layers learning more relevant features for the classification application. The effectiveness of deep learning models are hampered by limited data sets, skewed class distributions, and the undesirable "black box" of neural networks, which decreases their understandability and usability in precision medicine applications. This thesis addresses the challenge of building an explainable deep learning model for a clinical application: predicting the severity of Alzheimer's disease (AD). AD …
Predicting Severity Of Traumatic Brain Injury: A Residual Learning Model From Magnetic Resonance Images, Dacosta Yeboah
Predicting Severity Of Traumatic Brain Injury: A Residual Learning Model From Magnetic Resonance Images, Dacosta Yeboah
MSU Graduate Theses
One of the most significant frontiers for computational scientists is the engineering of human healthcare delivery based on intelligent analysis of health data. In a variety of neurological disorders such as Traumatic Brain Injury (TBI), neuro-imaging information plays a crucial role in the decision-making regarding patient care and as a potential prognostic marker for outcome. TBI is a heterogeneous neurological disorder. Due to the economic burdens of the disorder, sorting out this heterogeneity could provide more insights and better understanding of TBI recovery trajectories, thus improving overall diagnosis and treatment options. Magnetic Resonance Imaging (MRI) is a non-invasive technique that …
Synthesis And Characterization Of Gd-Doped Inp/Zns Quantum Dots For Use In Multimodal Imaging Probes, Molly Erin Duszynski
Synthesis And Characterization Of Gd-Doped Inp/Zns Quantum Dots For Use In Multimodal Imaging Probes, Molly Erin Duszynski
MSU Graduate Theses
Quantum dots (QDs), which are intensely fluorescent nanocrystals ranging 2-10 nanometers in diameter, have shown promise in fluorescence imaging. However, in vivo applications of QDs are limited due to the opaque surrounding of tissue and bones. In this study, InP/ZnS QDs were doped with a paramagnetic atom in an attempt to render them MRI-active. We have further bioconjugated these nanoprobes to develop highly specific MRI-active probes that can be used for detection of neurodegenerative diseases. These bioconjugated nanoprobes detect a mutated form of alpha-synuclein that forms oligomers that are a hallmark of Parkinson’s disease andother alpha-synucleinopathies. Here, we have optimized …