Anomaly Detection in Medical Imaging -- A Mini Review

TL;DR

This paper reviews the use of anomaly detection methods in medical imaging, emphasizing semi-supervised and unsupervised approaches to reduce reliance on labeled data and highlighting successful applications in brain MRI and potential in other domains.

Contribution

It provides a comprehensive literature review of anomaly detection in medical imaging, categorizing applications, summarizing key results, and offering guidance for future research.

Findings

Most research aims to reduce labeled data dependency.

Significant success in brain MRI anomaly detection.

Potential for applications in OCT and chest X-ray.

Abstract

The increasing digitization of medical imaging enables machine learning based improvements in detecting, visualizing and segmenting lesions, easing the workload for medical experts. However, supervised machine learning requires reliable labelled data, which is is often difficult or impossible to collect or at least time consuming and thereby costly. Therefore methods requiring only partly labeled data (semi-supervised) or no labeling at all (unsupervised methods) have been applied more regularly. Anomaly detection is one possible methodology that is able to leverage semi-supervised and unsupervised methods to handle medical imaging tasks like classification and segmentation. This paper uses a semi-exhaustive literature review of relevant anomaly detection papers in medical imaging to cluster into applications, highlight important results, establish lessons learned and give further advice on how to approach anomaly detection in medical imaging. The qualitative analysis is based on google scholar and 4 different search terms, resulting in 120 different analysed papers. The main results showed that the current research is mostly motivated by reducing the need for labelled data. Also, the successful and substantial amount of research in the brain MRI domain shows the potential for applications in further domains like OCT and chest X-ray.