title: Human vs machine: evaluation of fluorescence micrographs.
abstract: To enable high-throughput screening of molecular phenotypes, multi-parameter fluorescence microscopy is applied. Object of our study is lymphocytes which invade human tissue. One important basis for our collaborative project is the development of methods for automatic and accurate evaluation of fluorescence micrographs. As a part of this, we focus on the question of how to measure the accuracy of microscope image interpretation, by human experts or a computer system. Following standard practice we use methods motivated by receiver operator characteristics to discuss the accuracies of human experts and of neural network-based algorithms. For images of good quality the algorithms achieve the accuracy of the medium-skilled experts. In images with increased noise, the classifiers are outperformed by some of the experts. Furthermore, the neural network-based cell detection is much faster than the human experts.
authors.affiliation: Neuroinformatics Group, Faculty of Technology, University of Bielefeld, P O Box 100131, D-33501, Bielefeld, Germany. tnattkemper@techfak.uni-bielefeld.de
authors.forename: Tim W
authors.initials: TW
authors.lastname: Nattkemper
authors.forename: Thorsten
authors.initials: T
authors.lastname: Twellmann
authors.forename: Helge
authors.initials: H
authors.lastname: Ritter
authors.forename: Walter
authors.initials: W
authors.lastname: Schubert
mesh_terms: D000465:Algorithms; D000911:Antibodies, Monoclonal; D000954:Antigens, Surface / Q000032:analysis; D019992:Databases as Topic; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008214:Lymphocytes / Q000473:pathology*; D008856:Microscopy, Fluorescence; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity
chemical_list: D000911:Antibodies, Monoclonal; D000954:Antigens, Surface