title: Modern computing methods
title: harnessing AI, graph theory, and quantum computing
titleType: Subtitle
Creators: name: Lê, Nam Hoàng
Publisher: name: University of Iowa
Year: 2026
Subjects: algorithm
artificial intelligence
data science
image segmentation
quantum computing
Medical Imaging

description: The rapid increase in the amount of digital information worldwide comes with demands for various computational tasks. This presents a distinct opportunity to develop and apply computing techniques capable of revealing meaningful insights concealed within the intricate layers of information complexity. In order to effectively employ contemporary computing techniques in domain-specific computational tasks, it is essential to address the critical task of analyzing the requirements of the task and subsequently devising a suitable approach to tackle it. The emergence of machine learning, deep learning, graph theory, and lately quantum computing has been instrumental in addressing these challenges across various domains.
description: In this thesis, we first explore the practicality of using black-box machine learning models to provide useful insights into predictors and characteristics of hospital admissions for fall injuries in older patients. Local agnostic methods such as feature importance based on a mean decrease in the Gini index and logistic regression coefficients are crucial in the understanding of model-based learned predictions. On the other hand, there exist global explainable techniques that serve as universal probing tools. Specifically, model surrogate, drop-one column feature importance, and model ensemble, are capable of offering a comprehensive understanding across all machine learning models. By utilizing those tools, we successfully created a set of 18 distinct patterns, or signatures, that aid in identifying older individuals who are prone to experiencing fall-related injuries and may require hospital admission. In the second study, machine learning techniques were used to explore the factors that contribute to suicide deaths involving firearms. By combining the predictions of the top-performing models and producing an equivalent surrogate decision tree, combinations of high-risk factors predictive of suicide deaths in males by firearms were identified. This information can be used to develop an early detection tool and tailored prevention strategies for vulnerable individuals, particularly those residing in regions with high firearm ownership rates.
description: One of the major tasks in medical imaging is the segmentation of anatomical structures of interest. We developed graph-support deep learning tools to assist the segmentation of unruptured intracranial aneurysms. The first iteration of the tool allows for semi-automated segmentation with user inputs such as control points defining key locations of aneurysms and their vasculature. The second version of the tool called DeepLOGISMOS applies the self-training strategy to alleviate the lack of quality training labels and LOGISMOS post-processing module to improve segmentation masks. The proposed methods achieve high segmentation performance on five-fold cross-validation and superior detection sensitivity of 96% on an in-house dataset.
description: Last but not least, we explore the use of quantum computing, an emerging technology, in its feasibility to solve LOGISMOS single surface detection problem. Traditionally, this task requires solving for a minimum closed set in a directed graph. We proposed to transform the problem into a QUBO formulation and solve it using QAOA. We demonstrated that the proposed method can achieve comparable results to the classical approach on small simulation data. The results encourage further exploration of quantum computing in image processing and other demanding computing tasks.