An innovative deep learning-empowered paradigm for precise biological sample quantification

Wei, Y., Hu,D., Bibi, K., Abbasi, S. M. T., Li, L., Qu, F., Mehmood, N., Ho, Y. P., Ho, H. P., & Yuan, W. (2024). An innovative deep learning-empowered paradigm for precise biological sample quantification. In CLEO (Ed.). Proceedings: CLEO 2024. CLEO 2024, Charlotte, North Carolina, United States. OPTICA.

URI

https://opg.optica.org/abstract.cfm?URI=CLEO_AT-2024-AF1B.6

http://hdl.handle.net/20.500.11861/23957

Type

Conference Paper

Abstract

We present an innovative deep learning-aided paradigm that enables real-time and automated detection and classification of GFP (Green fluorescence protein)-labeled microreactor, overcoming the limitations of conventional methods.

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An innovative deep learning-empowered paradigm for precise biological sample quantification

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