Deep Learning based Cell Classification in Imaging Flow Cytometer

Authors

  • Yi Gu School of Artificial Intelligence and Computer Science, Jiangnan University, 214122, China
  • Aiguo Chen School of Artificial Intelligence and Computer Science, Jiangnan University, 214122, China
  • Xin Zhang School of Artificial Intelligence and Computer Science, Jiangnan University, 214122, China
  • Chao Fan School of Artificial Intelligence and Computer Science, Jiangnan University, 214122, China https://orcid.org/0000-0003-2216-7576
  • Kang Li School of Artificial Intelligence and Computer Science, Jiangnan University, 214122, China https://orcid.org/0000-0003-1246-3339
  • Jinsong Shen Jiangsu Saideli Diagnostic Technology Co., Ltd, Jingjiang, Jiangsu 214500, China; Jiangsu Saideli Pharmaceutical Machinery Manufacturing Co., Ltd, Jingjiang, Jiangsu 214500, China https://orcid.org/0000-0002-4960-3449

DOI

https://doi.org/10.52810/TPRIS.2021.100050

Keywords:

Deep learning, Image classification, Neural network, Imaging flow cytometer

Abstract

Deep learning is an idea technique for image classification. Imaging flow cytometer enables high throughput cell image acquisition and some have integrated with real-time cell sorting. The combination of deep learning and imaging flow cytometer has changed the landscape of high throughput cell analysis research. In this review, we focus on deep learning technologies applied in imaging flow cytometer for cell classification and real-time cell sorting. This article describes some recent research, challenges and future trend in this area.  

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Author Biographies

Yi Gu, School of Artificial Intelligence and Computer Science, Jiangnan University, 214122, China

Yi Gu received the B.S degree in information Engineering from Shanghai Jiao Tong University, Shanghai, China, in 2012, and the M.S degree in Applied Physics from Cornell University, US, in 2014, and Ph.D degree in Electrical Engineering from University of California San Diego, US, in 2019. He is currently an Assistant Professor with the School of Artificial Intelligence and Computer Science, Jiangnan University. His current research interests include bioinformatics, artificial intelligence, image processing and their applications.

Aiguo Chen, School of Artificial Intelligence and Computer Science, Jiangnan University, 214122, China

Aiguo Chen received the B.S degree in Computer Science and Technology from China University of Mining and Technology, Xuzhou, China, in 1998, and the M.S degree in Light Industry Technology and Engineering from Jiangnan University, Wuxi, China, in 2007, and Ph.D degree in Light Industry Information Technolog from Jiangnan University, Wuxi, China, in 2017. He is currently an Assistant Professor with the School of Artificial Intelligence and Computer Science, Jiangnan University. His current research interests include machine learning, artificial intelligence, image processing and their applications.

Xin Zhang, School of Artificial Intelligence and Computer Science, Jiangnan University, 214122, China

Xin Zhang received the Ph.D. degree in South China University of Technology, China. She is currently a lecturer in the School of Artificial Intelligence and Computer Science, Jiangnan University, China. Her research interests include evolutionary computation and their applications in intelligent manufacturing.

Chao Fan, School of Artificial Intelligence and Computer Science, Jiangnan University, 214122, China

Chao Fan received a M. S degree in Informatics from Peking University, Beijing, China, in 2012, and the Ph.D degree in Engineering from the University of Tokyo, Japan, in 2018. He is currently an Assistant Professor with the School of Artificial Intelligence and Computer Science, Jiangnan University. His research interests involve artificial intelligence, network science, and image processing.

Kang Li, School of Artificial Intelligence and Computer Science, Jiangnan University, 214122, China

Kang Li received the B.S degree in Computer science and technology from Northeastern University, Shenyang, China, in 2018.  He is currently pursuing the M.D. degree with School of Artificial Intelligence and Computer Science, Jiangnan University. His research area includes computational intelligence, image processing and their applications on medicine.

Jinsong Shen, Jiangsu Saideli Diagnostic Technology Co., Ltd, Jingjiang, Jiangsu 214500, China; Jiangsu Saideli Pharmaceutical Machinery Manufacturing Co., Ltd, Jingjiang, Jiangsu 214500, China

Jinsong Shen received the B.S degree from Tongji University, Shanghai, China. He is currently a CEO of Jiangsu Saideli Diagnostic Technology Co., Ltd.

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Deep Learning based Cell Classification in Imaging Flow Cytometer

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Published

2021-06-13

How to Cite

Gu, Y., Chen, A. ., Zhang, X. ., Fan, C. ., Li, K. ., & Shen, J. . (2021). Deep Learning based Cell Classification in Imaging Flow Cytometer. ASP Transactions on Pattern Recognition and Intelligent Systems, 1(2), 18–27. https://doi.org/10.52810/TPRIS.2021.100050

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Regular Paper