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Fig. 6 | eLight

Fig. 6

From: Computational coherent Raman scattering imaging: breaking physical barriers by fusion of advanced instrumentation and data science

Fig. 6

Deep learning for CRS image denoising and segmentation. a Principles of SS-ResNet image restoration and spectral unmixing. GT, ground truth. b Comparison between raw, SS-ResNet denoising and GT. The hyperspectral images are decomposed into chemical maps of protein, cholesterol and fatty acid. Scale bars, 20 µm. c Structure and workflow of training and validation of ResNet34 for the segmentation of SRS histology images. d SRS histological images of neoplastic (top) and normal (bottom) larynx tissue and the network classification results. a, b are from reference [36] and c, d are from reference [106]

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