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

Abstract : 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 species have similar morphologies. In this work, we report a deep learning-enabled high-throughput system for predicting Cryptosporidium and Giardia in drinking water. This system combines imaging flow
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https://hal-upec-upem.archives-ouvertes.fr/hal-03161362
Contributor : Tarik Bourouina <>
Submitted on : Saturday, March 6, 2021 - 1:24:03 PM
Last modification on : Friday, June 18, 2021 - 4:24:03 PM

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Shaobo Luo, Tarik Bourouina, Giovanni Chierchia, Hugues Talbot, Ai-Qun Liu, et al.. Deep learning‐enabled imaging flow cytometry for high‐speed Cryptosporidium and Giardia detection. Cytometry Part A, Wiley, 2021, ⟨10.1002/cyto.a.24321⟩. ⟨hal-03161362⟩

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