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Dokumenttyp:
Journal Article 
Autor(en):
Baur, Christoph; Wiestler, Benedikt; Muehlau, Mark; Zimmer, Claus; Navab, Nassir; Albarqouni, Shadi 
Titel:
Modeling Healthy Anatomy with Artificial Intelligence for Unsupervised Anomaly Detection in Brain MRI. 
Abstract:
Purpose: To develop an unsupervised deep learning model on MR images of normal brain anatomy to automatically detect deviations indicative of pathologic states on abnormal MR images. Materials and Methods: In this retrospective study, spatial autoencoders with skip-connections (which can learn to compress and reconstruct data) were leveraged to learn the normal variability of the brain from MR scans of healthy individuals. A total of 100 normal, in-house MR scans were used for training. Subseque...    »
 
Zeitschriftentitel:
Radiol Artif Intell 
Jahr:
2021 
Band / Volume:
Heft / Issue:
Volltext / DOI:
TUM Einrichtung:
Fachgebiet Neuroradiologie (Prof. Zimmer)