Position Regression for Unsupervised Anomaly Detection

Florentin Bieder, Julia Wolleb, Robin Sandkuehler, Philippe C. Cattin

Keywords: anomaly detection, position regression, unsupervised

TL;DR: We persent a novel, memory efficient way for unsupervised anomaly detection.

Abstract:

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.
The code will be publicly available.

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Paper Type: methodological development

Primary Subject Area: Detection and Diagnosis

Secondary Subject Area: Unsupervised Learning and Representation Learning

Confidentiality And Author Instructions: I read the call for papers and author instructions. I acknowledge that exceeding the page limit and/or altering the latex template can result in desk rejection.

Code And Data:

https://gitlab.com/cian.unibas.ch/position-regression
http://headctstudy.qure.ai/dataset