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Dokumenttyp:
Journal Article; Research Support, Non-U.S. Gov't; Review 
Autor(en):
Baur, Christoph; Denner, Stefan; Wiestler, Benedikt; Navab, Nassir; Albarqouni, Shadi 
Titel:
Autoencoders for unsupervised anomaly segmentation in brain MR images: A comparative study. 
Abstract:
Deep unsupervised representation learning has recently led to new approaches in the field of Unsupervised Anomaly Detection (UAD) in brain MRI. The main principle behind these works is to learn a model of normal anatomy by learning to compress and recover healthy data. This allows to spot abnormal structures from erroneous recoveries of compressed, potentially anomalous samples. The concept is of great interest to the medical image analysis community as it i) relieves from the need of vast amoun...    »
 
Zeitschriftentitel:
Med Image Anal 
Jahr:
2021 
Band / Volume:
69 
Print-ISSN:
1361-8415 
TUM Einrichtung:
Fachgebiet Neuroradiologie (Prof. Zimmer)