Deep Learning based Cell Classification in Imaging Flow Cytometer-Reference-Cited by-同舟云学术

Deep Learning based Cell Classification in Imaging Flow Cytometer

Published:2021-06-13 Issue:2 Volume:1 Page:18-27
ISSN:2788-6743
Container-title:ASP Transactions on Pattern Recognition and Intelligent Systems
language:en
Short-container-title:ASP Trans. Pattern Recognit. Intell. Syst.

Author:

Gu Yi¹^ORCID,Chen Aiguo¹^ORCID,Zhang Xin¹^ORCID,Fan Chao¹^ORCID,Li Kang¹^ORCID,Shen Jinsong²^ORCID

Affiliation:

1. School of Artificial Intelligence and Computer Science, Jiangnan University, 214122, China

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

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.

Publisher

Advancing Science Press Limited

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