Abstract: Objective To investigate the role of total polarization imaging and machine learning in the differential diagnosis of hepatocellular carcinoma and cholangiocarcinoma.Methods Polarization imaging was performed on 8 cases of poorly differentiated hepatocellular carcinoma and 8 cases of poorly differentiated cholangiocarcinoma.Pathologists selected three Regions of interest(ROIs) according to HE slices,measured the mueller matrix polarimetry of each ROI,and calculated a series of polarization basic parameters according to the mueller matrix  extraction analysis method.The polarization basic parameters were input into the artificial neural Network(ANN) model,and the 8-fold cross-validation method was used to train and verify the model in three categories.Results ANN model showed that the The precision and sensitive of differentiating hepatocellular carcinoma cells,Cholangiocarcinoma cells and other tissues based on mueller matrix polarimetry was 0.846-3 and 0.810-7.Conclusion The diagnostic model of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on polarized imaging and machine learning is of great value in pathological auxiliary diagnosis,which can provide help for clinical accurate diagnosis and treatment.

Key words: hepatocellular carcinoma, intrahepatic cholangiocarcinoma, mueller matrix polarimetry, artificial neural network