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
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