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Proceedings Paper

Finding novelty with uncertainty
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Paper Abstract

Medical images are often used to detect and characterize pathology and disease; however, automatically identifying and segmenting pathology in medical images is challenging because the appearance of pathology across diseases varies widely. To address this challenge, we propose a Bayesian deep learning method that learns to translate healthy computed tomography images to magnetic resonance images and simultaneously calculates voxel-wise uncertainty. Since high uncertainty occurs in pathological regions of the image, this uncertainty can be used for unsupervised anomaly segmentation. We show encouraging experimental results on an unsupervised anomaly segmentation task by combining two types of uncertainty into a novel quantity we call scibilic uncertainty.

Paper Details

Date Published: 10 March 2020
PDF: 6 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113130H (10 March 2020);
Show Author Affiliations
Jacob C. Reinhold, Johns Hopkins Univ. (United States)
Yufan He, Johns Hopkins Univ. (United States)
Shizhong Han, 12 Sigma Technologies (United States)
Yunqiang Chen, 12 Sigma Technologies (United States)
Dashan Gao, 12 Sigma Technologies (United States)
Junghoon Lee, Johns Hopkins School of Medicine (United States)
Jerry L. Prince, Johns Hopkins Univ. (United States)
Aaron Carass, Johns Hopkins Univ. (United States)


Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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