Keras R-CNN: library for cell detection in biological images using deep neural networks

Background A common yet still manual task in basic biology research, high-throughput drug screening and digital pathology is identifying the number, location, and type of individual cells in images. Object detection methods can be useful for identifying individual cells as well as their phenotype in...

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Veröffentlicht in:	BMC bioinformatics Jg. 21; H. 1; S. 300 - 7
Hauptverfasser:	Hung, Jane, Goodman, Allen, Ravel, Deepali, Lopes, Stefanie C. P., Rangel, Gabriel W., Nery, Odailton A., Malleret, Benoit, Nosten, Francois, Lacerda, Marcus V. G., Ferreira, Marcelo U., Rénia, Laurent, Duraisingh, Manoj T., Costa, Fabio T. M., Marti, Matthias, Carpenter, Anne E.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 11.07.2020 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Accuracy Algorithms Annotations Applications programming Artificial neural networks Bioinformatics Biological effects Biomedical and Life Sciences Cell cycle Cell Nucleus Classification Computational Biology/Bioinformatics Computer Appl. in Life Sciences Computer applications Convolutional networks Datasets Deep Learning Drug screening Experts Fluorescence Genotype & phenotype Humans Image analysis Image detection Image processing Image Processing, Computer-Assisted - methods Keras Libraries Life Sciences Machine learning Machine Learning and Artificial Intelligence in Bioinformatics Malaria Microarrays Neural networks Object detection Object recognition Parasites Phenotypes Plasmodium vivax - growth & development Software Vector-borne diseases Deep learning Keras Malaria Convolutional networks Object detection
ISSN:	1471-2105, 1471-2105
Online-Zugang:	Volltext
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