# Position Regression for Unsupervised Anomaly Detection

arXiv: https://arxiv.org/abs/2301.08064 · HTML: https://mlsanity.com/paper/2301.08064/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 11 references that are in the map.
- First posted on arXiv 19 Jan 2023.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study](https://mlsanity.com/paper/2004.03271/) (2020)
- [Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders](https://mlsanity.com/paper/1806.04972/) (2018)
- [Numerical Coordinate Regression with Convolutional Neural Networks](https://mlsanity.com/paper/1801.07372/) (2018)
- [Contrastive Learning of Relative Position Regression for One-Shot Object Localization in 3D Medical Images](https://mlsanity.com/paper/2012.07043/) (2020)
- [AirLab: Autograd Image Registration Laboratory](https://mlsanity.com/paper/1806.09907/) (2018)
- [Latent-Insensitive autoencoders for Anomaly Detection](https://mlsanity.com/paper/2110.13101/) (2021)

## Built on by (most cited first, 1 of 1)

- [Binary Noise for Binary Tasks: Masked Bernoulli Diffusion for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2403.11667/) (2024)

## Abstract (arXiv)

In recent years, anomaly detection has become an essential field in medical image analysis. Most current anomaly detection methods for medical images are based on image reconstruction. In this work, we propose a novel anomaly detection approach based on coordinate regression. Our method estimates the position of patches within a volume, and is trained only on data of healthy subjects. During inference, we can detect and localize anomalies by considering the error of the position estimate of a given patch. We apply our method to 3D CT volumes and evaluate it on patients with intracranial haemorrhages and cranial fractures. The results show that our method performs well in detecting these anomalies. Furthermore, we show that our method requires less memory than comparable approaches that involve image reconstruction. This is highly relevant for processing large 3D volumes, for instance, CT or MRI scans.