Expert-free eye alignment and machine learning for predictive health
Author(s)Swedish, Tristan Breaden
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
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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.
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).
DepartmentProgram in Media Arts and Sciences (Massachusetts Institute of Technology)
Massachusetts Institute of Technology
Program in Media Arts and Sciences ()