Assessing microscope image focus quality with deep learning

BMC Bioinformatics

Volumn 19, Issue 1, 2018, Pages

  Yang, Samuel J ^a   Berndl, Marc ^a   Ando, D Michael ^a   Barch, Mariya ^b   Narayanaswamy, Arunachalam ^a   Christiansen, Eric ^a   Hoyer, Stephan ^a   Roat, Chris ^a   Hung, Jane ^c,d   Rueden, Curtis T ^e   Shankar, Asim ^a   Finkbeiner, Steven ^b,f   Nelson, Philip ^a  

^a GOOGLE INC   (United States)

^b Taube Koret Center for Neurodegenerative Disease Research and DaedalusBio   (United States)

^c BROAD INSTITUTE OF MIT AND HARVARD   (United States)

^d Massachusetts Institute of Technology Cambridge   (United States)

^e UNIVERSITY OF WISCONSIN MADISON   (United States)

^f UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

CellProfiler; Deep learning; Defocus; Focus; Image analysis; Image quality; ImageJ; Machine learning; Open source

Indexed keywords

DEEP LEARNING; DEEP NEURAL NETWORKS; FOCUSING; FORECASTING; IMAGE ANALYSIS; LEARNING SYSTEMS; MICROSCOPES; OPEN SOURCE SOFTWARE; PROTEINS; QUALITY CONTROL;

AUTOMATED IMAGE ANALYSIS; AUTOMATED MICROSCOPE; CELLPROFILER; CLASSIFICATION TASKS; DEFOCUS; IMAGEJ; NEURAL NETWORK MODEL; OPEN SOURCES;

IMAGE QUALITY;

BONE TUMOR; DIAGNOSTIC IMAGING; HUMAN; IMAGE PROCESSING; MACHINE LEARNING; MICROSCOPY; OSTEOSARCOMA; PROCEDURES; SOFTWARE; TUMOR CELL CULTURE;

BONE NEOPLASMS; DIAGNOSTIC IMAGING; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MACHINE LEARNING; MICROSCOPY; OSTEOSARCOMA; SOFTWARE; TUMOR CELLS, CULTURED;

EID: 85043760273     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-018-2087-4     Document Type: Article

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