Fluorescence lifetime imaging endomicroscopy based ex-vivo lung cancer prediction using multi-scale concatenated-dilation convolutional neural networks

Deep learning technologies have been successfully applied to automatic diagnostics of ex-vivo lung cancer with fluorescence lifetime imaging endomicroscopy (FLIM). Recent advance in convolutional neural networks (CNNs) by splitting input features for multi-scale feature extraction as a feature-level aggregation, has achieved further improvement in visual recognition. However, due to the splitting, correlations among input features are no longer...