Hardware inspired neural network for efficient time-resolved biomedical imaging

Zang, Zhenya and Xiao, Dong and Wang, Quan and Jiao, Ziao and Li, Zinuo and Chen, Yu and Li, David Day-Uei; (2022) Hardware inspired neural network for efficient time-resolved biomedical imaging. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022 . IEEE, GBR, pp. 1883-1886. ISBN 9781728127828 (https://doi.org/10.1109/embc48229.2022.9871214)

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Abstract

Convolutional neural networks (CNN) have revealed exceptional performance for fluorescence lifetime imaging (FLIM). However, redundant parameters and complicated topologies make it challenging to implement such networks on embedded hardware to achieve real-time processing. We report a lightweight, quantized neural architecture that can offer fast FLIM imaging. The forward-propagation is significantly simplified by replacing matrix multiplications in each convolution layer with additions and data quantization using a low bit-width. We first used synthetic 3-D lifetime data with given lifetime ranges and photon counts to assure correct average lifetimes can be obtained. Afterwards, human prostatic cancer cells incubated with gold nanoprobes were utilized to validate the feasibility of the network for real-world data. The quantized network yielded a 37.8% compression ratio without performance degradation. Clinical relevance - This neural network can be applied to diagnose cancer early based on fluorescence lifetime in a non-invasive way. This approach brings high accuracy and accelerates diagnostic processes for clinicians who are not experts in biomedical signal processing

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