Cell anomaly localisation using structured uncertainty prediction networks

Vodenicharski, Boyko, McDermott, Samuel, Webber, Katherine, Introini, Viola, Cicuta, Pietro, Bowman, Richard, Simpson, Ivor J A and Campbell, Neill D F (2022) Cell anomaly localisation using structured uncertainty prediction networks. Medical Imaging with Deep Learning (MIDL 2022), Zürich, Switzerland, 6-8 July 2022. Published in: Proceedings of Machine Learning Research. 1285-1300. PMLR ISSN 2640-3498

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This paper proposes an unsupervised approach to anomaly detection in bright-field or fluorescence cell microscopy, where our goal is to localise malaria parasites. This is achieved by building a generative model (a variational autoencoder) that describes healthy cell images, where we additionally model the structure of the predicted image uncertainty, rather than assuming pixelwise independence in the likelihood function. This provides a “whitened” residual representation, where the anticipated structured mistakes by the generative model are reduced, but distinctive structures that did not occur in the training distribution, e.g. parasites are highlighted. We employ the recently published Structured Uncertainty Prediction Networks approach to enable tractable learning of the uncertainty structure. Here, the residual covariance matrix is efficiently approximated using a sparse Cholesky parameterisation. We demonstrate that our proposed approach is more effective for detecting real and synthetic structured image perturbations compared to diagonal Gaussian likelihoods.

Item Type: Conference Proceedings
Schools and Departments: School of Engineering and Informatics > Informatics
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 26 Jul 2022 16:05
Last Modified: 11 Jan 2023 12:59
URI: http://sro.sussex.ac.uk/id/eprint/107045

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