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Variational Auto-Encoders have often been used for unsupervised pretraining,feature extraction and out-of-distribution and anomaly detection in the medicalfield. However, VAEs often lack the ability to produce sharp images and learnhigh-level features. We propose to alleviate these issues by adding a newbranch to conditional hierarchical VAEs. This enforces a division betweenhigher-level and lower-level features. Despite the additional computationaloverhead compared to a normal VAE it results in sharper and betterreconstructions and can capture the data distribution similarly well (indicatedby a similar or slightly better OoD detection performance).

High- and Low-level image component decomposition using VAEs for improved reconstruction and anomaly detection