Deep learning-enabled imaging flow cytometry for high-speed Cryptosporidium and Giardia detection

• Main Authors: Luo, Shaobo, Nguyen, Kim Truc, Nguyen, Binh Thi Thanh, Feng, Shilun, Shi, Yuzhi, Elsayed, Ahmed, Zhang, Yi, Zhou, Xiaohong, Wen, Bihan, Chierchia, Giovanni, Talbot, Hugues, Bourouina, Tarik, Jiang, Xudong, Liu, Ai Qun
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Electrical and electronic engineering; Cell Classification; Deep Learning
• Online Access: https://hdl.handle.net/10356/155664
• Institution: Nanyang Technological University

Imaging flow cytometry has become a popular technology for bioparticle image analysis because of its capability of capturing thousands of images per second. Nevertheless, the vast number of images generated by imaging flow cytometry imposes great challenges for data analysis especially when the spec...