Unsupervised Lesion Detection in Brain CT using Bayesian Convolutional Autoencoders

Nick Pawlowski, Matthew C.H. Lee, Martin Rajchl, Steven McDonagh, Enzo Ferrante, Konstantinos Kamnitsas, Sam Cooke, Susan Stevenson, Aneesh Khetani, Tom Newman, Fred Zeiler, Richard Digby, Jonathan P. Coles, Daniel Rueckert, David K. Menon, Virginia F.J. Newcombe, Ben Glocker

Abstract: Normally, lesions are detected using supervised learning techniques that require labelled training data. We explore the use of Bayesian autoencoders to learn the variability of healthy tissue and detect lesions as unlikely events under the normative model. As a proof-of-concept, we test our method on registered 2D mid- axial slices from CT imaging data.Our results indicate that our method achieves best performance in detecting lesions caused by bleeding compared to baselines.

Keywords: deep learning, auto encoder, bayesian deep learning, outlier detection, abnormality detection, lesion detection

Author Affiliation: Imperial College, HeartFlow, University of Cambridge, Universidad Nacional del Litoral / CONICET (Argentina)