UNSUPERVISED DEEP LEARNING FOR DETECTION OF BRAIN DISEASE IN MR IMAGING

Background: Manual detection and interpretation of suspicious findings in radiological exams is a slow and lengthy process, requiring the highest level of attention and expertise. Introducing an automatic approach to distinguish abnormal from normal anatomy and physiology has the potential to speed...

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Bibliographic Details

Published in:	Clinical neuroradiology (Munich) Vol. 29; no. S1; p. S14
Main Authors:	Eisawy, Rami, Moosbauer, Julia, Finck, Tom, Zimmer, Claus, Menze, Bjorn, Pfister, Franz M.J, Wiestler, Benedikt
Format:	Journal Article
Language:	English
Published:	Springer 01.09.2019
Subjects:	Algorithms Machine learning Magnetic resonance imaging Medical research Medicine, Experimental Physiological aspects
ISSN:	1869-1439
Online Access:	Get full text
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