Know When You Don't Know: A Robust Deep Learning Approach in the Presence of Unknown Phenotypes

Assay and Drug Development Technologies
Oliver DürrBeate Sick

Abstract

Deep convolutional neural networks show outstanding performance in image-based phenotype classification given that all existing phenotypes are presented during the training of the network. However, in real-world high-content screening (HCS) experiments, it is often impossible to know all phenotypes in advance. Moreover, novel phenotype discovery itself can be an HCS outcome of interest. This aspect of HCS is not yet covered by classical deep learning approaches. When presenting an image with a novel phenotype to a trained network, it fails to indicate a novelty discovery but assigns the image to a wrong phenotype. To tackle this problem and address the need for novelty detection, we use a recently developed Bayesian approach for deep neural networks called Monte Carlo (MC) dropout to define different uncertainty measures for each phenotype prediction. With real HCS data, we show that these uncertainty measures allow us to identify novel or unclear phenotypes. In addition, we also found that the MC dropout method results in a significant improvement of classification accuracy. The proposed procedure used in our HCS case study can be easily transferred to any existing network architecture and will be beneficial in terms of accura...Continue Reading

References

Oct 17, 2003·Nature·Won-Ki HuhErin K O'Shea
May 29, 2015·Nature·Yann LeCunGeoffrey Hinton
Mar 8, 2016·Journal of Biomolecular Screening·Oliver Dürr, Beate Sick
Jun 17, 2016·Bioinformatics·Oren Z KrausBrendan J Frey
Apr 20, 2017·Molecular Systems Biology·Oren Z KrausBrenda J Andrews
Sep 1, 2017·Nature Methods·Juan C CaicedoAnne E Carpenter
Sep 29, 2017·Molecular Biology of the Cell·Christoph SommerDaniel W Gerlich
Jan 18, 2018·Drug Research·Daniel SiegismundStephan Steigele

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Citations

Nov 12, 2020·PLoS Computational Biology·Teresa Maria Rosaria NovielloLuigi Cerulo
May 24, 2021·Current Opinion in Chemical Biology·Aditya PratapaJuan C Caicedo

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Software Mentioned

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