Abstract: Unsupervised Anomaly Localization Using Variational Auto-Encoders
- Resource Type
- Authors
- Simon A. A. Kohl; Jens Petersen; Klaus H. Maier-Hein; Fabian Isensee; David Zimmerer
- Source
- Informatik aktuell ISBN: 9783658292669
Bildverarbeitung für die Medizin
- Subject
- Computer science
business.industry
Deep learning
Auto encoders
Unsupervised learning
Pattern recognition
Artificial intelligence
Anomaly (physics)
business
Image (mathematics)
- Language
An assumption-free automatic check of medical images for potentially overseen anomalies would be a valuable assistance for a radiologist. Deep learning and especially Variational Auto-Encoders (VAEs) have shown great potential in the unsupervised learning of data distributions. In principle, this allows for such a check and even the localization of parts in the image that are most suspicious.