Open AccessDeep Learning based Cell Classification in Imaging Flow Cytometer
Author(s) -
Yi Gu,
Aiguo Chen,
Zhang Xin,
Chao Fan,
Kang Li,
Jinsong Shen
Publication year - 2021
Publication title -
asp transactions on pattern recognition and intelligent systems
Language(s) - English
Resource type - Journals
ISSN - 2788-6743
DOI - 10.52810/tpris.2021.100050
Subject(s) - deep learning , sorting , computer science , throughput , artificial intelligence , focus (optics) , flow (mathematics) , cell sorting , cell , chemistry , telecommunications , physics , optics , wireless , biochemistry , geometry , mathematics , programming language
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.