Fig. 7
Deep learning for CRS-specific applications. a A 1D neural network for non-resonant CARS background removal. Examples of simulated raw CARS spectra (blue), true Im(χR (3)) (green) and network-predicted Im(χR (3)) (red) are shown. CL convolutional layers, FC fully connected layers. b Schematic of the U-within-U network. c Left, hyperspectral SRS image (maximum intensity projection) of an unlabeled live lung cancer cell. Right top row, network predicted fluorescence labels of nuclei, mitochondria and endoplasmic reticulum. Right bottom row, fluorescence images after staining. Scale bar, 25 μm. d Schematic of single-shot femtosecond SRS mapping of intracellular organelles using deep learning. Orange arrows indicate training set generation, green arrows represent training validation, and blue arrows stand for testing. (e) Illustration of U-net prediction of two-color picosecond SRS images (2845 & 2930 cm−1) using single-shot femtosecond SRS. Example prediction results on gastric tissue are shown. Scale bars, 50 µm. a is from reference [112], panels b, c are from reference [116], d is from reference [56] and e is from reference [118]