2019/11/27 by David Zimmerer, Zimmerer, David, Jens Petersen +3 · 2 citations
Computer Science · Medicine · #Anomaly Detection Techniques and Applications #COVID-19 diagnosis using AI #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Healthcare #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1911.12161
openalex publication_date 2019/11/27 · openalex created_date 2019/12/05 · openalex updated_date 2026/07/28
Variational Auto-Encoders have often been used for unsupervised pretraining, feature extraction and out-of-distribution and anomaly detection in the medical field. However, VAEs often lack the ability to produce sharp images and learn high-level features. We propose to alleviate these issues by adding a new branch to conditional hierarchical VAEs. This enforces a division between higher-level and lower-level features. Despite the additional computational overhead compared to a normal VAE it results in sharper and better reconstructions and can capture the data distribution similarly well (indicated by a similar or slightly better OoD detection performance).