On the robustness of machine learning algorithms toward microfluidic distortions for cell classification <i>via</i> on-chip fluorescence microscopy-Reference-Cited by-同舟云学术

On the robustness of machine learning algorithms toward microfluidic distortions for cell classification via on-chip fluorescence microscopy

Published:2022 Issue:18 Volume:22 Page:3453-3463
ISSN:1473-0197
Container-title:Lab on a Chip
language:en
Short-container-title:Lab Chip

Author:

Ahmad Ali¹²,Sala Federico³⁴^ORCID,Paiè Petra⁴^ORCID,Candeo Alessia³^ORCID,D'Annunzio Sarah⁵,Zippo Alessio⁵,Frindel Carole²,Osellame Roberto³⁴^ORCID,Bragheri Francesca⁴,Bassi Andrea³⁴,Rousseau David¹^ORCID

Affiliation:

1. Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), UMR INRAE IRHS, Université d'Angers, 62 Avenue Notre Dame du Lac, 49000 Angers, France

2. Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), CNRS UMR 5220 – INSERM U1206, Université Lyon 1, Insa de Lyon, Lyon, France

3. Department of Physics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy

4. Istituto di Fotonica e Nanotecnologie, CNR, Piazza Leonardo da Vinci 32, 20133 Milano, Italy

5. CIBIO, University of Trento, 38123 Trento, Italy

Abstract

Machine learning algorithms for cell classification via on-chip fluorescence microscopy are shown to be robust to microfluidic distortions due to cell displacement during acquisition.

Funder

H2020 Future and Emerging Technologies

Publisher

Royal Society of Chemistry (RSC)

Subject

Biomedical Engineering,General Chemistry,Biochemistry,Bioengineering

Link

http://pubs.rsc.org/en/content/articlepdf/2022/LC/D2LC00482H

Reference54 articles.

1. Flow cytometry: principles, applications and recent advances

2. Review: imaging technologies for flow cytometry

3. Microfluidic chip-based technologies: emerging platforms for cancer diagnosis