A Versatile Deep Learning Architecture for Classification and Label-Free Prediction of Hyperspectral Images.
A Versatile Deep Learning Architecture for Classification and Label-Free Prediction of Hyperspectral Images.
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DOI：
10.1038/s42256-021-00309-y
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发表时间：
2021-04
影响因子：
23.8
通讯作者：
Fu D
中科院分区：
文献类型：
--
作者：
Manifold B;Men S;Hu R;Fu D
高光谱成像是一种技术，它提供了丰富的化学或成分信息，这是传统成像方式（如基于光的反射、透射或发射的强度成像或彩色成像）通常无法获得的。高光谱成像的分析往往依赖于机器学习方法来提取信息。在这里，我们提出了一种新的灵活架构，即U-within-U-Net，它可以在各种高光谱成像技术上对正交成像模式进行分类、分割和预测。具体而言，我们在大鼠肝组织质谱成像中展示了印第安松高光谱数据集的特征分割和分类，以及多种药物的同时定位预测。我们进一步证明了从高光谱激发拉曼散射显微镜图像的无标记荧光图像预测。u - in- u - net架构适用于输入和输出尺寸和数据源差别很大的各种数据集，这表明它在推进高光谱成像在从遥感、医学成像到显微镜等许多不同应用领域的应用方面具有巨大潜力。
Hyperspectral imaging is a technique that provides rich chemical or compositional information not regularly available to traditional imaging modalities such as intensity imaging or color imaging based on the reflection, transmission, or emission of light. Analysis of hyperspectral imaging often relies on machine learning methods to extract information. Here, we present a new flexible architecture, the U-within-U-Net, that can perform classification, segmentation, and prediction of orthogonal imaging modalities on a variety of hyperspectral imaging techniques. Specifically, we demonstrate feature segmentation and classification on the Indian Pines hyperspectral dataset and simultaneous location prediction of multiple drugs in mass spectrometry imaging of rat liver tissue. We further demonstrate label-free fluorescence image prediction from hyperspectral stimulated Raman scattering microscopy images. The applicability of the U-within-U-Net architecture on diverse datasets with widely varying input and output dimensions and data sources suggest that it has great potential in advancing the use of hyperspectral imaging across many different application areas ranging from remote sensing, to medical imaging, to microscopy.
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影响因子：
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作者：
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通讯作者：
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