Work

Accurate, fast and memory efficient quantification of immune cell phenotypes in cytometry using machine learning

Year: 2024

Type: preprint

Source: bioRxiv (Cold Spring Harbor Laboratory)

DOI: 10.1101/2024.07.26.605341

Authors: Tarik Exner, Nicolaj S. Hackert, Frederic Pohl, Goerkem Osmanusta, Fee Schmidt, Hanns-Martin Lorenz, Guido Wabnitz, Georg Schett, Frederik Graw, Joerg Henes, Ricardo Grieshaber-Bouyer

Institutions: Heidelberg University, University Hospital Heidelberg, Universitätsklinikum Tübingen, University of Tübingen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Universitätsklinikum Erlangen

Language: English

Topic: Cell Image Analysis Techniques

Subfield: Biophysics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: closed

OpenAlex ID: W4401159111

Cites: 35

Cited by:

Related to: 10