# Unsupervised Anomaly Localization using Variational Auto-Encoders

## TL;DR

This paper introduces a novel VAE-based method for unsupervised anomaly localization in medical images that improves performance by incorporating a KL-divergence term, enabling assumption-free detection and localization.

## Contribution

It proposes a new formalism combining VAE reconstruction with KL-divergence for assumption-free anomaly localization, validated on medical and benchmark datasets.

## Key findings

- Outperforms state-of-the-art VAE localization methods
- Shows robust performance across hyperparameters
- Achieves competitive maximum performance

## Abstract

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. Currently, however, the reconstruction-based localization by design requires adjusting the model architecture to the specific problem looked at during evaluation. This contradicts the principle of building assumption-free models. We propose complementing the localization part with a term derived from the Kullback-Leibler (KL)-divergence. For validation, we perform a series of experiments on FashionMNIST as well as on a medical task including >1000 healthy and >250 brain tumor patients. Results show that the proposed formalism outperforms the state of the art VAE-based localization of anomalies across many hyperparameter settings and also shows a competitive max performance.