Date of Award
12-2023
Document Type
Thesis
Degree Name
Master of Science in Operations Research
Department
Department of Operational Sciences
First Advisor
Nathan B. Gaw, PhD
Abstract
Saliency maps are a widely used methodology to make deep learning models more interpretable. They provide post-hoc explanations through identification of the most pertinent areas of an input medical image. These techniques have been assessed based on 1) localization utility, 2) sensitivity to model weight randomization, 3) repeatability, and 4) reproducibility. Ten saliency map techniques will be tested, Grad, Smooth Grad, Integrated Gradients, Smooth Integrated Gradients, XRAI, Grad-CAM, Guided Backpropagation, Guided GradCAM, GradCAM++, and ScoreCAM. The neural networks used to predict and read medical information require a reliable solution to provide medical practitioners intelligible results. Using the information of two, large, publicly available radiology datasets, we look to quantify and posit a solution to this problem so that the usage of saliency maps in the high-risk domain of medical imaging will require less scrutiny and can hold a more trustworthy base.
AFIT Designator
AFIT-ENS-MS-23-D-010
Recommended Citation
Skelly, Nolan C., "An Improved Saliency Map with Trustworthiness for Localizing Abnormalities in Medical Imaging" (2023). Theses and Dissertations. 7676.
https://scholar.afit.edu/etd/7676
Included in
Bioimaging and Biomedical Optics Commons, Other Operations Research, Systems Engineering and Industrial Engineering Commons
Comments
A 12-month embargo was observed for this thesis.
Approved for public release; PA case number on file.