Xinwei Gao1, Yanfeng Liu1, Yong Guo1

  • 1State Key Laboratory of Radio Frequency Heterogeneous Integration (Shenzhen University); College of Physics and Optoelectronic Engineering, Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province Shenzhen University, Shenzhen 518060, P. R. China.

Analytical chemistry
|July 11, 2025
PubMed
概括

使用1D频道注意力卷积神经网络 (1D CANNs) 的新深度学习方法显著加快了光终身成像 (FLIM) 分析. 这种高效的方法减少了计算负载,并提高了生物医学应用的准确性.