Anomaly Detection in Medical Imaging -- A Mini Review

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.

Machine Learning–Based Anomaly Detection Techniques in Ophthalmology

“…In medical literature, AD algorithms have been applied to various image modalities such as magnetic resonance imaging, chest radiography, and mammography. 4 Within ophthalmology, the first AD model used an unsupervised 1-class support vector machine to detect anomalies on optical coherence tomography images. 5 Recent studies have also looked at other image modalities such as retinal fundus photography, using newer deep learning frameworks such as convolution neural networks and generative adversarial networks.…”

Anomaly Detection in Medical Time Series with Generative Adversarial Networks: A Selective Review

“…Traditionally, statistical and rule-based methods have been employed for anomaly detection in medical time series data [4,7,8]. These methods rely on predefined thresholds, statistical models, or expert knowledge to identify deviations from normal.…”