拉曼光谱
高光谱成像
深度学习
吞吐量
人工智能
计算机科学
化学成像
降噪
材料科学
模式识别(心理学)
纳米技术
光学
物理
电信
无线
作者
Conor C. Horgan,Magnus Jensen,Anika Nagelkerke,Jean-Phillipe St-Pierre,Tom Vercauteren,Molly M. Stevens,Mads S. Bergholt
出处
期刊:Cornell University - arXiv
日期:2020-09-28
被引量:1
摘要
Raman spectroscopy enables non-destructive, label-free imaging with unprecedented molecular contrast but is limited by slow data acquisition, largely preventing high-throughput imaging applications. Here, we present a comprehensive framework for higher-throughput molecular imaging via deep learning enabled Raman spectroscopy, termed DeepeR, trained on a large dataset of hyperspectral Raman images, with over 1.5 million spectra (400 hours of acquisition) in total. We firstly perform denoising and reconstruction of low signal-to-noise ratio Raman molecular signatures via deep learning, with a 9x improvement in mean squared error over state-of-the-art Raman filtering methods. Next, we develop a neural network for robust 2-4x super-resolution of hyperspectral Raman images that preserves molecular cellular information. Combining these approaches, we achieve Raman imaging speed-ups of up to 160x, enabling high resolution, high signal-to-noise ratio cellular imaging in under one minute. Finally, transfer learning is applied to extend DeepeR from cell to tissue-scale imaging. DeepeR provides a foundation that will enable a host of higher-throughput Raman spectroscopy and molecular imaging applications across biomedicine.
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