  Classifying hepatocellular carcinoma (HCC) and intrahepatic
cholangiocarcinoma (ICC) is a critical step in treatment selection and
prognosis evaluation for patients with liver diseases. Traditional
histopathological diagnosis poses challenges in this context. In this study, we
introduce a novel polarization and radiomics feature fusion network, which
combines polarization features obtained from Mueller matrix images of liver
pathological samples with radiomics features derived from corresponding
pathological images to classify HCC and ICC. Our fusion network integrates a
two-tier fusion approach, comprising early feature-level fusion and late
classification-level fusion. By harnessing the strengths of polarization
imaging techniques and image feature-based machine learning, our proposed
fusion network significantly enhances classification accuracy. Notably, even at
reduced imaging resolutions, the fusion network maintains robust performance
due to the additional information provided by polarization features, which may
not align with human visual perception. Our experimental results underscore the
potential of this fusion network as a powerful tool for computer-aided
diagnosis of HCC and ICC, showcasing the benefits and prospects of integrating
polarization imaging techniques into the current image-intensive digital
pathological diagnosis. We aim to contribute this innovative approach to
top-tier journals, offering fresh insights and valuable tools in the fields of
medical imaging and cancer diagnosis. By introducing polarization imaging into
liver cancer classification, we demonstrate its interdisciplinary potential in
addressing challenges in medical image analysis, promising advancements in
medical imaging and cancer diagnosis.
