Expert-free eye alignment and machine learning for predictive health
Name
1012944585-MIT.pdf
Description
Full printable version
Size
6.82 MB
Format
Adobe PDF
Checksum (MD5)
dac1817f73dd210e1597480c52502d70
Author(s)
Swedish, Tristan Breaden
Advisor(s)
Ramesh Raskar.
Date Issued
2017
Publisher
Massachusetts Institute of Technology
Abstract
This thesis documents the development of an "expert-free" device in order to realize a system for scalable screening of the eye fundus. The goal of this work is to demonstrate enabling technologies that remove dependence on expert operators and explore the usefulness of this approach in the context of scalable health screening. I will present a system that includes a novel method for eye self-alignment and automatic image analysis and evaluate its effectiveness when applied to a case study of a diabetic retinopathy screening program. This work is inspired by advances in machine learning that makes accessible interactions previously confined to specialized environments and trained users. I will also suggest some new directions for future work based on this expert-free paradigm.
Description
Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 67-72).
Subjects
Program in Media Arts and Sciences ()
MIT Department
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
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