Date of Award
2025
Document Type
Open Access Dissertation
Degree Name
Doctor of Philosophy in Computational Science and Engineering (PhD)
Administrative Home Department
Department of Applied Computing
Advisor 1
Guy C. Hembroff
Advisor 2
Sidike Paheding
Committee Member 1
Laura E. Brown
Committee Member 2
Dukka B. KC
Abstract
Medical Image Segmentation is a critical task in the field of medical imaging, playing a crucial role in diagnostics, treatment planning, and disease monitoring. The emergence of Deep Learning (DL) has ushered in a new era in Artificial Intelligence (AI), propelling remarkable advancements in key domains like language translation, object recognition, and recommendation systems. This evolution has been accompanied by continuous enhancements in computational efficiency and improvements in predictive accuracy. The introduction of sophisticated algorithms, such as convolutional neural networks (CNNs) and transformers, exemplifies these advancements. DL algorithms have demonstrated exceptional efficacy in medical image segmentation tasks, showcasing the potential for AI-driven early diagnostics. However, the deployment of AI systems in clinical environments is often hindered by the substantial computational demands and complexity of cutting-edge DL models. In this research proposal, we explore various methodologies to enrich the visual feature representation for medical images. We focus on integrating global context-oriented techniques, such as attention mechanisms, into the development of parameter-efficient deep learning models. Our goal is to create a generalized, end-to-end medical image segmentation framework that can accurately and efficiently segment medical images across different modalities and conditions. By leveraging advanced deep learning techniques and optimizing model architectures, we aim to enhance the performance and generalization capabilities of medical image segmentation models, ultimately contributing to improved clinical outcomes
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Reyes-Angulo, Abel A., "GENERALIZING MEDICAL IMAGE SEGMENTATION TASK WITH EFFICIENT DEEP LEARNING MODELS", Open Access Dissertation, Michigan Technological University, 2025.
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