Computer Science ETDs

Publication Date

Winter 12-17-2021

Abstract

This dissertation addresses gaps in artificial intelligence-based computer vision tasks in the medical image processing field. We demonstrate effective methods for standardizing and augmenting digital fundus photographs so that robust convolutional neural network-based systems can perform high-throughput disease classification and generalize to never-before-seen data from novel camera technologies, scaling with the changing hardware landscape, as well as keeping up with vast amount of incoming data from the ever-increasing population. We also tackle the problem of discovering relevant samples in an unlabeled cohort of image data, thus widening the bottleneck to all downstream supervised machine learning tasks.

Language

English

Keywords

Artificial Intelligence, Biomedical Engineering, Data Discovery, Diabetic Retinopathy, Machine Learning

Document Type

Dissertation

Degree Name

Computer Science

Level of Degree

Doctoral

Department Name

Department of Computer Science

First Committee Member (Chair)

Trilce Estrada

Second Committee Member

Shuang Luan

Third Committee Member

Abdullah Mueen

Fourth Committee Member

Manel Martinez-Ramon

Fifth Committee Member

Peter Soliz

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