Generative Method to Discover Genetically Driven Image Biomarkers
Author(s)Cho, Michael; Estepar, Raul San Jose; Batmanghelich, Nematollah; Saeedi, Ardavan; Golland, Polina
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We present a generative probabilistic approach to discovery of disease subtypes determined by the genetic variants. In many diseases, multiple types of pathology may present simultaneously in a patient, making quantification of the disease challenging. Our method seeks common co-occurring image and genetic patterns in a population as a way to model these two different data types jointly. We assume that each patient is a mixture of multiple disease subtypes and use the joint generative model of image and genetic markers to identify disease subtypes guided by known genetic influences. Our model is based on a variant of the so-called topic models that uncover the latent structure in a collection of data. We derive an efficient variational inference algorithm to extract patterns of co-occurrence and to quantify the presence of heterogeneous disease processes in each patient. We evaluate the method on simulated data and illustrate its use in the context of Chronic Obstructive Pulmonary Disease (COPD) to characterize the relationship between image and genetic signatures of COPD subtypes in a large patient cohort.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Information Processing in Medical Imaging
Batmanghelich, Nematollah K. et al. “Generative Method to Discover Genetically Driven Image Biomarkers.”Ourselin S., Alexander D., Westin CF., Cardoso M., editors. Information Processing in Medical Imaging. IPMI 2015. Lecture Notes in Computer Science, 9123 (2015): 30–42. © 2015 Springer International Publishing Switzerland
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