Automated Zebrafish Phenotype Pattern Recognition: 6 Years Ago, and Now.

biomedical image recognition computer vision deep learning feature extraction high-throughput screening pattern recognition zebrafish

Journal

Zebrafish
ISSN: 1557-8542
Titre abrégé: Zebrafish
Pays: United States
ID NLM: 101225070

Informations de publication

Date de publication:
12 2022
Historique:
pubmed: 7 9 2022
medline: 22 12 2022
entrez: 6 9 2022
Statut: ppublish

Résumé

The article assesses the developments in automated phenotype pattern recognition: Potential spikes in classification performance, even when facing the common small-scale biomedical data set, and as a reader, you will find out about changes in the development effort and complexity for researchers and practitioners. After reading, you will be aware of the benefits and unreasonable effectiveness and ease of use of an automated end-to-end deep learning pipeline for classification tasks of biomedical perception systems.

Identifiants

pubmed: 36067119
doi: 10.1089/zeb.2022.0027
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

213-217

Auteurs

Mark Schutera (M)

Institute for Automation and Applied Informatics (IAI), Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen, Germany.

Luca Rettenberger (L)

Institute for Automation and Applied Informatics (IAI), Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen, Germany.

Markus Reischl (M)

Institute for Automation and Applied Informatics (IAI), Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen, Germany.

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Classifications MeSH