TY - JOUR U1 - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed) A1 - Dürr, Oliver A1 - Murina, Elvis A1 - Siegismund, Daniel A1 - Tolkachev, Vasily A1 - Steigele, Stephan A1 - Sick, Beate T1 - Know when you don't know BT - A robust deep learning approach in the presence of unknown phenotypes JF - ASSAY and Drug Development Technologies N2 - 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 accuracy and novelty detection. KW - Screening KW - Classification KW - Deep learning KW - Imaging Y1 - 2018 UR - https://www.liebertpub.com/doi/10.1089/adt.2018.859 SN - 1540-658X SS - 1540-658X U6 - https://doi.org/10.1089/adt.2018.859 DO - https://doi.org/10.1089/adt.2018.859 VL - Vol. 16 IS - No. 6 SP - 343 EP - 349 ER -