While generative adversarial networks (GAN) have been widely applied in various settings, the competitive deep learning frameworks such as GANs were not as popular in medical image processing and even less widely applied on high resolution data due to the issues related to their stability. In this dissertation, we examined optimal ways of modeling a generalizable competitive framework that can alleviate the inherent stability issues while still meeting additional objectives such as to achieve prediction accuracy of a classification task or to satisfy other performance metrics on high dimensional data sets.
The first part of the thesis is focused on exploring better network performance in a non-competitive setting with a closed-form solution. (1) We introduced Pyramid Encoder in seq2seq models and observed a significant increase in computational and memory efficiency while achieving a similar repair rate to their non-pyramid counterparts. (2) We proposed a mixed spatio-temporal neural network for real-time prediction of crimes, establishing the feasibility of a convolutional neural network (CNN) in the spatio-temporal domain. (3) We developed and validated an interpretable deep learning framework for Alzheimer’s disease (AD) classification as a clinically adaptable strategy to generate neuroimaging signatures for AD diagnosis and as a generalizable approach for linking deep learning to pathophysiological processes in human disease. (4) We designed and validated an end-to-end survival model for prediction of progression from mild cognitive impairment (MCI) to AD, and identified regions salient to predicting progression from MCI to AD. (5) Additionally, we applied a supervised learning framework in Parrondo's Paradox that maps playing history directly to the decision space, and learned to combine two individually-losing games to have a positive expectation.
The second part is focused on the design and analysis of neural models in a competitive setting without a closed-form solution. We extended the models from tackling a single objective to multiple tasks, while also moving from two-dimensional images to three-dimensional magnetic resonance imaging scans of the human brain. (1) We experimented with domain-specific inpainting with a concurrently pre-trained GAN to recover noised or cropped images. (2) We developed a GAN model to enhance MRI-driven AD classification performance using generative adversarial learning. (3) Finally, we proposed a competitive framework that could recover 3D medical data from 2D slices, while retaining disease-related information. / 2023-07-04T00:00:00Z
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/44832 |
| Date | 05 July 2022 |
| Creators | Zhou, Xiao |
| Contributors | Chin, Sang P., Kolachalama, Vijaya B. |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/ |
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