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Classifying hepatocellular carcinoma (HCC) and intrahepaticcholangiocarcinoma (ICC) is a critical step in treatment selection andprognosis evaluation for patients with liver diseases. Traditionalhistopathological diagnosis poses challenges in this context. In this study, weintroduce a novel polarization and radiomics feature fusion network, whichcombines polarization features obtained from Mueller matrix images of liverpathological samples with radiomics features derived from correspondingpathological images to classify HCC and ICC. Our fusion network integrates atwo-tier fusion approach, comprising early feature-level fusion and lateclassification-level fusion. By harnessing the strengths of polarizationimaging techniques and image feature-based machine learning, our proposedfusion network significantly enhances classification accuracy. Notably, even atreduced imaging resolutions, the fusion network maintains robust performancedue to the additional information provided by polarization features, which maynot align with human visual perception. Our experimental results underscore thepotential of this fusion network as a powerful tool for computer-aideddiagnosis of HCC and ICC, showcasing the benefits and prospects of integratingpolarization imaging techniques into the current image-intensive digitalpathological diagnosis. We aim to contribute this innovative approach totop-tier journals, offering fresh insights and valuable tools in the fields ofmedical imaging and cancer diagnosis. By introducing polarization imaging intoliver cancer classification, we demonstrate its interdisciplinary potential inaddressing challenges in medical image analysis, promising advancements inmedical imaging and cancer diagnosis.

A Polarization and Radiomics Feature Fusion Network for the Classification of Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma