Contrastive Representations for UnsupervisedAnomaly Detection and Localization

Carsten Tim Lüth, David Zimmerer, Gregor Koehler, Paul F Jaeger, Fabian Isensee, Jens Petersen, Klaus Maier-Hein

Keywords: Anomaly Detection, Self-supervised Learning, Contrastive Training

TL;DR: We propose a self-supervised approach for unsupervised anomaly detection and localisation on MRI brain scans.

Abstract:

Unsupervised anomaly detection in medical imaging aims to detect and localize arbitrary anomalies without requiring labels during training. Generally, this is achieved by learning a data distribution of normal samples and detecting anomalies as regions in the image which deviate from this distribution. In the medical imaging domain, most current state-of-the-art methods use latent variable generative models. Because such models operate directly on sample space, they tend to primarily encode low-level statistics (like pixel intensities),
while having problems capturing fine semantic information within their representations. Recent work has shown that representations obtained from a feature extractor trained with a discriminative task are rich in semantic information. This, however, requires labeled datasets - a prerequisite that is often not fulfilled. We propose CRADL, a framework for unsupervised anomaly detection and localization consisting of a feature extractor trained with a contrastive pretext-task and a generative model which learns the distribution of representations. Through this, we circumvent the need for labels while still being able to fit the generative model on semantic-rich representations. We further compare the quality of these contrastive representations with representations obtained from a VAE and ceVAE in the context of anomaly localization. We evaluate CRADL on the BraTS and ISLES datasets, as well as an in-house dataset, and demonstrate state-of-the-art performance on the task of anomaly localization in our comparison with a VAE and ceVAE.

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Paper Type: methodological development

Primary Subject Area: Unsupervised Learning and Representation Learning

Secondary Subject Area: Uncertainty Estimation

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