Denoising Autoencoders for Unsupervised Anomaly Detection in Brain MRI

Antanas Kascenas, Nicolas Pugeault, Alison Q O'Neil

Keywords: Anomaly detection, Unsupervised learning, Autoencoder, Denoising, MRI

TL;DR: We propose a simple but effective denoising autoencoder method using coarse intensity noise for unsupervised anomaly detection in brain MRI.

Abstract: Pathological brain lesions exhibit diverse appearance in brain images, making it difficult to design specialized detection solutions due to the lack of comprehensive data and annotations. Thus, in this work we tackle unsupervised anomaly detection, using only healthy data for training with the aim of detecting unseen anomalies at test time. Many current approaches employ autoencoders with restrictive architectures (i.e. containing information bottlenecks) that tend to give poor reconstructions of not only the anomalous but also the normal parts of the brain. Instead, we investigate classical denoising autoencoder models that do not require bottlenecks and can employ skip connections to give high resolution fidelity. We design a simple noise generation method of upscaling low-resolution noise that enables high-quality reconstructions, reducing false positive noise in reconstruction errors. We find that with appropriate noise generation, denoising autoencoder reconstruction errors generalize to hyperintense lesion segmentation and can reach state of the art performance for unsupervised tumor detection in brain MRI data, beating more complex methods such as variational autoencoders. We believe this provides a strong and easy-to-implement baseline for further research into unsupervised anomaly detection.

Registration: I acknowledge that publication of this at MIDL and in the proceedings requires at least one of the authors to register and present the work during the conference.

Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.

Paper Type: methodological development

Primary Subject Area: Unsupervised Learning and Representation Learning

Secondary Subject Area: Detection and Diagnosis

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.