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Quantifying the deformability of malaria-infected red blood cells using deep learning trained on synthetic cells.

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Bibliographic Details
Title:	Quantifying the deformability of malaria-infected red blood cells using deep learning trained on synthetic cells.
Authors:	Rademaker DT; Medical BioSciences, Radboud University Medical Center, 6525 GA Nijmegen, the Netherlands., Koopmans JJ; Medical BioSciences, Radboud University Medical Center, 6525 GA Nijmegen, the Netherlands., Thyen GMSM; Radboud Center for Infectious Diseases, Medical Microbiology, Radboud University Medical Center, 6525 GA Nijmegen, the Netherlands., Piruska A; Institute for Molecules and Materials, Radboud University, 6525 AJ Nijmegen, the Netherlands., Huck WTS; Institute for Molecules and Materials, Radboud University, 6525 AJ Nijmegen, the Netherlands., Vriend G; Baco Institute for Protein Science, Mindoro 5201, Philippines., 't Hoen PAC; Medical BioSciences, Radboud University Medical Center, 6525 GA Nijmegen, the Netherlands., Kooij TWA; Radboud Center for Infectious Diseases, Medical Microbiology, Radboud University Medical Center, 6525 GA Nijmegen, the Netherlands., Huynen MA; Medical BioSciences, Radboud University Medical Center, 6525 GA Nijmegen, the Netherlands., Proellochs NI; Radboud Center for Infectious Diseases, Medical Microbiology, Radboud University Medical Center, 6525 GA Nijmegen, the Netherlands.
Source:	IScience [iScience] 2023 Nov 23; Vol. 26 (12), pp. 108542. Date of Electronic Publication: 2023 Nov 23 (Print Publication: 2023).
Publication Type:	Journal Article
Journal Info:	Publisher: Cell Press Country of Publication: United States NLM ID: 101724038 Publication Model: eCollection Cited Medium: Internet ISSN: 2589-0042 (Electronic) Linking ISSN: 25890042 NLM ISO Abbreviation: iScience Subsets: PubMed not MEDLINE
Database:	MEDLINE Ultimate

Description
ISSN:	2589-0042
DOI:	10.1016/j.isci.2023.108542