Unsupervised discovery of emphysema subtypes in a large clinical cohort
Name
964450467-MIT.pdf
Description
Full printable version
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4.27 MB
Format
Adobe PDF
Checksum (MD5)
039cf0edfeecfb66aa1958e5d3010c87
Author(s)
Binder, Polina
Advisor(s)
Polina Golland.
Date Issued
2016
Publisher
Massachusetts Institute of Technology
Abstract
Emphysema is one of the hallmarks of Chronic Obstructive Pulmonary Disease (COPD), a devastating lung disease often caused by smoking. Emphysema appears on Computed Tomography (CT) scans as a variety of textures that correlate with the disease subtypes. It has been shown that the disease subtypes and the lung texture are linked to physiological indicators and prognosis, although neither is well characterized clinically. Most previous computational approaches to modeling emphysema imaging data have focused on supervised classification of lung textures in patches of CT scans. In this work, we describe a generative model that jointly captures heterogeneity of disease subtypes and of the patient population. We also derive a corresponding inference algorithm that simultaneously discovers disease subtypes and population structure in an unsupervised manner. This approach enables us to create image-based descriptors of emphysema beyond those that can be identified through manual labeling of currently defined phenotypes. By applying the resulting algorithm to a large data set, we identify groups of patients and disease subtypes that correlate with distinct physiological indicators.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 45-47).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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