Deep learning-based diatom taxonomy on virtual slides.

ABSTRACT: Deep convolutional neural networks are emerging as the state of the art method for supervised classification of images also in the context of taxonomic identification. Different morphologies and imaging technologies applied across organismal groups lead to highly specific image domains, which need customization of deep learning solutions. Here we provide an example using deep convolutional neural networks (CNNs) for taxonomic identification of the morphologically diverse microalgal group of diatoms. Using a combination of high-resolution slide scanning microscopy, web-based collaborative image annotation and diatom-tailored image analysis, we assembled a diatom image database from two Southern Ocean expeditions. We use these data to investigate the effect of CNN architecture, background ma

SUBMITTER: Kloster M 

PROVIDER: S-EPMC7468105 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature