Computational single-cell classification using deep learning on bright-field and phase images

Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017

Volumn , Issue , 2017, Pages 190-193

  Meng, Nan ^a   So, Hayden K H ^a   Lam, Edmund Y ^a  

^a UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; CYTOLOGY; DEEP LEARNING; IMAGE CLASSIFICATION; NETWORK ARCHITECTURE;

AUTOMATED CELL; BRIGHT FIELDS; HIGH-ACCURACY; HUMAN INTERVENTION; PHASE IMAGE; SINGLE CELLS; TIME STRETCH; ULTRA-FAST;

CELLS;

EID: 85027845354     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.23919/MVA.2017.7986833     Document Type: Conference Paper

References (17)

1. A. Krizhevsky, I. Sutskever, and G. E. Hinton, \Imagenet classication with deep convolutional neural networks," in Advances in Neural Information Processing Systems, 2012, pp. 1097-1105.
2. M. D. Zeiler and R. Fergus, \Visualizing and understanding convolutional networks," in European Con-ference on Computer Vision, September 2014, pp. 818-833.
3. R. Girshick, J. Donahue, T. Darrell, and J. Malik, \Rich feature hierarchies for accurate object detection and semantic segmentation," in IEEE Conference on Computer Vision and Pattern Recognition, June 2014, pp. 580-587.
4. R. Girshick, \Fast R-CNN," in IEEE International Conference on Computer Vision, February 2015, pp. 1440-1448.
5. P. Perner, H. Perner, and B. Muller, \Mining knowledge for HEp-2 cell image classication," Artificial In-telligence in Medicine, vol. 26, no. 1, pp. 161-173, October 2002.
6. N. Theera-Umpon, \White blood cell segmentation and classication in microscopic bone marrow images," in International Conference on Fuzzy Systems and Kn-owledge Discovery, vol. 3614, August 2005, pp. 787-796.
7. P. Foggia, G. Percannella, P. Soda, and M. Vento, \Early experiences in mitotic cells recognition on HEp-2 slides," in IEEE International Symposium on Com-puter-based Medical Systems, October 2010, pp. 38-43.
8. R. Nosaka and K. Fukui, \HEp-2 cell classication using rotation invariant co-occurrence among local binary patterns," Pattern Recognition, vol. 47, no. 7, pp. 2428-2436, July 2014.
9. N. Bayramoglu, J. Kannala, and J. Heikkila, \Human epithelial type 2 cell classication with convolutional neural networks," in IEEE International Conference on Bioinformatics and Bioengineering, January 2016, pp. 1-6.
10. Z. Gao, J. Zhang, L. Zhou, and L. Wang, \HEp-2 cell image classication with convolutional neural networks," in IEEE Workshop on Pattern Recognition Techniques for Indirect Immunouorescence Images, December 2014, pp. 24-28.
11. H. Li, J. Zhang, and W.-S. Zheng, \Deep CNNs for HEp-2 cells classication: A cross-specimen analysis," arXiv:1604.05816, April 2016.
12. A. K. Lau et al., \Interferometric time-stretch microscopy for ultrafast quantitative cellular and tissue imaging at 1 m," Journal of Biomedical Optics, vol. 19, p. 076001, July 2014.
13. J. Wu et al., \Ultrafast laser-scanning time-stretch imaging at visible wavelengths," Light: Science & Ap-plications, vol. 6, p. e16196, January 2017.
14. T. T.Wong et al., \Asymmetric-detection time-stretch optical microscopy (ATOM) for high-contrast and highspeed microuidic cellular imaging," Scientific Reports, vol. 4, p. 3656, January 2014.
15. J. Xie, X. Niu, A. K. Lau, K. K. Tsia, and H. K. So, \Accelerated cell imaging and classication on FPGAs for quantitative-phase asymmetric-detection time-stretch optical microscopy," in IEEE International Con-ference on Field Programmable Technology, December 2016, pp. 1-8.
16. S. Ioe and C. Szegedy, \Batch normalization: Accelerating deep network training by reducing internal covariate shift," arXiv:1502.03167, March 2015.
17. Y. Jia et al., \Cae: Convolutional architecture for fast feature embedding," in ACM International Con-ference on Multimedia, November 2014, pp. 675-678.