Chapter title |
Unsupervised Discovery of Emphysema Subtypes in a Large Clinical Cohort
|
---|---|
Chapter number | 22 |
Book title |
Machine Learning in Medical Imaging
|
Published in |
Lecture notes in computer science, October 2016
|
DOI | 10.1007/978-3-319-47157-0_22 |
Pubmed ID | |
Book ISBNs |
978-3-31-947156-3, 978-3-31-947157-0
|
Authors |
Polina Binder, Nematollah K. Batmanghelich, Raul San Jose Estepar, Polina Golland |
Editors |
Li Wang, Ehsan Adeli, Qian Wang, Yinghuan Shi, Heung-Il Suk |
Abstract |
Emphysema is one of the hallmarks of Chronic Obstructive Pulmonary Disorder (COPD), a devastating lung disease often caused by smoking. Emphysema appears on Computed Tomography (CT) scans as a variety of textures that correlate with disease subtypes. It has been shown that the disease subtypes and textures 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 describe 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. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 26 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 6 | 23% |
Researcher | 4 | 15% |
Professor > Associate Professor | 4 | 15% |
Lecturer | 2 | 8% |
Other | 2 | 8% |
Other | 3 | 12% |
Unknown | 5 | 19% |
Readers by discipline | Count | As % |
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Medicine and Dentistry | 5 | 19% |
Computer Science | 3 | 12% |
Business, Management and Accounting | 2 | 8% |
Agricultural and Biological Sciences | 1 | 4% |
Other | 0 | 0% |
Unknown | 10 | 38% |