Quantitative characterization of eosinophilia in nasal polyps with AI‐based single cell classification
Eosinophilic granulocytes have characteristic morphological features. This makes them prime candidates for utilization of a single cell binary classification network. Single cell binary classification networks can reliably help quantify eosinophils in nasal polyps.
Authors
- I Christiansen (ORCID: https://orcid.org/0000-0001-6115-2890)
- Anne‐Sophie Homøe (ORCID: https://orcid.org/0009-0009-9716-6501)
- Katalin Kiss (ORCID: https://orcid.org/0000-0003-4599-067X)
- T. Jensen (ORCID: https://orcid.org/0000-0001-5622-9347)
- Kasper Aanæs (ORCID: https://orcid.org/0000-0002-7353-0817)
- Vibeke Backer (ORCID: https://orcid.org/0000-0002-7806-7219)
- Martin Stampe (ORCID: https://orcid.org/0009-0001-2803-9463)
- Jens Even Tidemandsen (ORCID: https://orcid.org/0009-0006-6924-1218)
- Rasmus Hartvig
- Sigrid Louise Hjorth Rasmussen
- Emilie Neumann Nielsen
Institutions
- Roskilde University (DK)
- University of Copenhagen (DK)
- Rigshospitalet (DK)
Publication Details
- Journal
- International Forum of Allergy & Rhinology
- Published
- 2024-09-20
- DOI
- https://doi.org/10.1002/alr.23454
- Citations
- 2
- Primary Topic
- Allergic Rhinitis and Sensitization
- Type
- article
- Field-Weighted Citation Impact
- 0.77