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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Vydané v:	Clinical neuroradiology (Munich) Ročník 29; číslo S1; s. S14
Hlavní autori:	Eisawy, Rami, Moosbauer, Julia, Finck, Tom, Zimmer, Claus, Menze, Bjorn, Pfister, Franz M.J, Wiestler, Benedikt
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Springer 01.09.2019
Predmet:	Algorithms Machine learning Magnetic resonance imaging Medical research Medicine, Experimental Physiological aspects
ISSN:	1869-1439
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