Single-cell phenotype classification using deep convolutional neural networks

Journal of Biomolecular Screening

Volumn 21, Issue 9, 2016, Pages 998-1003

  Dürr, Oliver ^a   Sick, Beate ^a  

^a ZURICH UNIVERSITY OF APPLIED SCIENCES   (Switzerland)

Author keywords

cell based assays; deep learning; high content screening; single cell classification

Indexed keywords

CARDENOLIDE; DIGOXIN; FENBENDAZOLE; FLUPHENAZINE; LANATOSIDE C; METOCLOPRAMIDE; OXIBENDAZOLE; PACLITAXEL; PERUVOSIDE; PROCAINE;

ALGORITHM; ARTICLE; CELL NUCLEUS; CONTROLLED STUDY; DEEP CONVOLUTIONAL NEURAL NETWORK; DISCRIMINANT ANALYSIS; DRUG MECHANISM; ENDOPLASMIC RETICULUM; GOLGI COMPLEX; HUMAN; LEARNING; MACHINE LEARNING; MITOCHONDRION; NERVE CELL NETWORK; OBJECT RELATION; PATTERN RECOGNITION; PHENOTYPE; PRIORITY JOURNAL; RANDOM FOREST; SUPPORT VECTOR MACHINE; ARTIFICIAL NEURAL NETWORK; IMAGE PROCESSING; PROCEDURES; SINGLE CELL ANALYSIS; SOFTWARE; STATISTICS AND NUMERICAL DATA;

ALGORITHMS; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MACHINE LEARNING; NEURAL NETWORKS (COMPUTER); SINGLE-CELL ANALYSIS; SOFTWARE; SUPPORT VECTOR MACHINE;

EID: 84988526163     PISSN: 10870571     EISSN: 1552454X     Source Type: Journal    
DOI: 10.1177/1087057116631284     Document Type: Article

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