Hardware Inspired Neural Network for Efficient Time-Resolved Biomedical Imaging.
Hardware Inspired Neural Network for Efficient Time-Resolved Biomedical Imaging.
复制标题
用于高效时间分辨生物医学成像的硬件启发神经网络。
DOI：
10.1109/embc48229.2022.9871214
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Zang Z
中科院分区：
文献类型：
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作者：
Zang Z
卷积神经网络（CNN）在荧光寿命成像（FLIM）方面表现优异。然而，冗余的参数和复杂的拓扑结构给在嵌入式硬件上实现这种网络以实现实时处理带来了挑战。我们报告了一种轻量级的、量化的神经结构，可以提供快速的胶片成像。通过使用低位宽的加法和数据量化取代每个卷积层中的矩阵乘法，大大简化了前向传播。我们首先使用合成三维寿命数据，给定寿命范围和光子计数，以确保正确的平均寿命可以得到。随后，利用金纳米探针孵育的人类前列腺癌细胞来验证该网络在现实世界数据中的可行性。量化网络在没有性能下降的情况下产生37.8%的压缩比。临床相关性-该神经网络可以应用于基于荧光寿命的非侵入性早期癌症诊断。该方法为非生物医学信号处理专家的临床医生提供了高准确性和加速诊断过程。
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.