Unsupervised Brain Anomaly Detection and Segmentation with Transformers

TL;DR

This paper introduces a novel unsupervised method combining vector quantised variational autoencoders and autoregressive transformers for detecting and segmenting brain anomalies, achieving superior results with modest data and computational resources.

Contribution

It presents a new approach that leverages transformers and autoencoders for efficient, unsupervised brain anomaly detection and segmentation, outperforming existing methods.

Findings

Superior anomaly detection performance on real brain lesions

Effective with small datasets and low computational cost

No post-processing required for segmentation

Abstract

Pathological brain appearances may be so heterogeneous as to be intelligible only as anomalies, defined by their deviation from normality rather than any specific pathological characteristic. Amongst the hardest tasks in medical imaging, detecting such anomalies requires models of the normal brain that combine compactness with the expressivity of the complex, long-range interactions that characterise its structural organisation. These are requirements transformers have arguably greater potential to satisfy than other current candidate architectures, but their application has been inhibited by their demands on data and computational resource. Here we combine the latent representation of vector quantised variational autoencoders with an ensemble of autoregressive transformers to enable unsupervised anomaly detection and segmentation defined by deviation from healthy brain imaging data, achievable at low computational cost, within relative modest data regimes. We compare our method to current state-of-the-art approaches across a series of experiments involving synthetic and real pathological lesions. On real lesions, we train our models on 15,000 radiologically normal participants from UK Biobank, and evaluate performance on four different brain MR datasets with small vessel disease, demyelinating lesions, and tumours. We demonstrate superior anomaly detection performance both image-wise and pixel-wise, achievable without post-processing. These results draw attention to the potential of transformers in this most challenging of imaging tasks.