Automated classification of hepatocellular carcinoma differentiation using multiphoton microscopy and deep learning.

In the case of hepatocellular carcinoma (HCC) samples, classification of differentiation is crucial for determining prognosis and treatment strategy decisions. However, a label-free and automated classification system for HCC grading has not been yet developed. Hence, in this study, we demonstrate the fusion of multiphoton microscopy and a deep-learning algorithm for classifying HCC differentiation to produce an innovative computer-aided diagnostic method. Convolutional neural networks based on the VGG-16 framework were trained using 217 combined two-photon excitation fluorescence and second-harmonic generation images; the resulting classification accuracy of the HCC differentiation grade was over 90%. Our results suggest that a combination of multiphoton microscopy and deep learning can realize label-free, automated methods for various tissues, diseases and other related classification problems.