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Volumn 7, Issue 1, 2017, Pages

Automated Training of Deep Convolutional Neural Networks for Cell Segmentation

Author keywords

[No Author keywords available]

Indexed keywords

NERVOUS SYSTEM; ARTIFICIAL NEURAL NETWORK; CYTOLOGY; HUMAN; IMAGE PROCESSING; INTRAVITAL MICROSCOPY; LABORATORY AUTOMATION; MACHINE LEARNING; PROCEDURES; TUMOR CELL LINE;

EID: 85027498089     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/s41598-017-07599-6     Document Type: Article
Times cited : (128)

References (19)
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    • 84999836246 scopus 로고    scopus 로고
    • Deep learning automates the quantitative analysis of individual cells in live-cell imaging experiments
    • Van Valen, D. A. et al. Deep learning automates the quantitative analysis of individual cells in live-cell imaging experiments. PLoS Comput Biol 12, e1005177 (2016).
    • (2016) PLoS Comput Biol , vol.12 , pp. e1005177
    • Van Valen, D.A.1
  • 6
    • 85027508892 scopus 로고    scopus 로고
    • Deep learning for imaging flow cytometry: Cell cycle analysis of jurkat cells
    • Eulenberg, P. et al. Deep learning for imaging flow cytometry: Cell cycle analysis of jurkat cells. bioRxiv 081364 (2016).
    • (2016) BioRxiv , pp. 081364
    • Eulenberg, P.1
  • 7
    • 33845792555 scopus 로고    scopus 로고
    • Cellprofiler: Image analysis software for identifying and quantifying cell phenotypes
    • Carpenter, A. E., Jones, T. R. & Lamprecht, M. R. et al. Cellprofiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biology 7, R100 (2006).
    • (2006) Genome Biology , vol.7 , pp. R100
    • Carpenter, A.E.1    Jones, T.R.2    Lamprecht, M.R.3
  • 9
    • 84891388151 scopus 로고    scopus 로고
    • Multiplex cytological profiling assay to measure diverse cellular states
    • Gustafsdottir, S. M. et al. Multiplex cytological profiling assay to measure diverse cellular states. PloS one 8, e80999 (2013).
    • (2013) PloS One , vol.8 , pp. e80999
    • Gustafsdottir, S.M.1
  • 10
    • 84863198481 scopus 로고    scopus 로고
    • Annotated high-throughput microscopy image sets for validation
    • Ljosa, V., Sokolnicki, K. L. & Carpenter, A. E. Annotated high-throughput microscopy image sets for validation. Nat Methods 9, 637 (2012).
    • (2012) Nat Methods , vol.9 , pp. 637
    • Ljosa, V.1    Sokolnicki, K.L.2    Carpenter, A.E.3
  • 11
    • 0011384911 scopus 로고    scopus 로고
    • A comparison of methods for estimation of intensity non uniformities in 2d and 3d microscope images of fluorescence stained cells
    • Lindblad, J. & Bengtsson, E. A comparison of methods for estimation of intensity non uniformities in 2d and 3d microscope images of fluorescence stained cells. In Proceedings of the Scandinavian Conference On Image Analysis 264-271 (2001).
    • (2001) Proceedings of the Scandinavian Conference on Image Analysis , pp. 264-271
    • Lindblad, J.1    Bengtsson, E.2
  • 17
    • 84893343292 scopus 로고    scopus 로고
    • Lecture 6. 5-rmsprop: Divide the gradient by a running average of its recent magnitude
    • Tieleman, T. & Hinton, G. Lecture 6. 5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning (2012).
    • (2012) COURSERA: Neural Networks for Machine Learning
    • Tieleman, T.1    Hinton, G.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.