@inproceedings{860209d3ddaa4f098fe7e43eb362fefd,
title = "A High Performance of Single Cell Imaging Detection with Deep Learning",
abstract = "Single cell imaging enables new applications such as biomedical diagnostics, food inspection, and water quality monitoring. In this paper, we study the cell imaging-based machine learning techniques for high-performance cell detection. By taking the advantage of deep learning and imaging flow cytometry, we manage to detect Cryptosporidium and Giardia cells in the bright-field images with high accuracy and high speed on embedded GPU system. Our experiments demonstrate that the newly developed deep learning-based algorithms surpasses the hand-crafted features and SVM-based algorithms. We achieved above 99 percentage in accuracy and 580+fps in speed on embedded Jetson TX2 platform. Our research will lead to a highly accurate real-time single cell level detection system in future.",
keywords = "cell imaging, CNN, deep learning, embedded",
author = "Luo, {S. B.} and Nguyen, {K. T.} and Jiang, {X. D.} and Wu, {J. F.} and Wen, {B. H.} and Y. Zhang and G. Chierchia and H. Talbot and T. Bourouina and Kwong, {D. L.} and Liu, {A. Q.}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 4th IEEE International Conference on Image, Vision and Computing, ICIVC 2019 ; Conference date: 05-07-2019 Through 07-07-2019",
year = "2019",
month = jul,
doi = "10.1109/ICIVC47709.2019.8981372",
language = "English",
series = "2019 IEEE 4th International Conference on Image, Vision and Computing, ICIVC 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "356--360",
booktitle = "2019 IEEE 4th International Conference on Image, Vision and Computing, ICIVC 2019",
}