# A Case for the Score: Identifying Image Anomalies using Variational Autoencoder Gradients

arXiv: https://arxiv.org/abs/1912.00003 · HTML: https://mlsanity.com/paper/1912.00003/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2019.

## Built on by (most cited first, 2 of 2)

- [Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Survey](https://mlsanity.com/paper/2207.10298/) (2022)
- [Stack of discriminative autoencoders for multiclass anomaly detection in endoscopy images](https://mlsanity.com/paper/2103.08508/) (2021)

## Abstract (arXiv)

Through training on unlabeled data, anomaly detection has the potential to impact computer-aided diagnosis by outlining suspicious regions. Previous work on deep-learning-based anomaly detection has primarily focused on the reconstruction error. We argue instead, that pixel-wise anomaly ratings derived from a Variational Autoencoder based score approximation yield a theoretically better grounded and more faithful estimate. In our experiments, Variational Autoencoder gradient-based rating outperforms other approaches on unsupervised pixel-wise tumor detection on the BraTS-2017 dataset with a ROC-AUC of 0.94.