User: Guest  Login
Document type:
Journal Article; Research Support, Non-U.S. Gov't; Review 
Author(s):
Baur, Christoph; Denner, Stefan; Wiestler, Benedikt; Navab, Nassir; Albarqouni, Shadi 
Title:
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...    »
 
Journal title abbreviation:
Med Image Anal 
Year:
2021 
Journal volume:
69 
Print-ISSN:
1361-8415 
TUM Institution:
Fachgebiet Neuroradiologie (Prof. Zimmer)