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.

通过将来自肝脏病理样本的 Mueller 矩阵图像的极化特征与相应病理图像的纹理特征相结合，引入了一种新的极化和辐射学特征融合网络来对肝细胞癌（HCC）和肝内胆管癌（ICC）进行分类。在图像分辨率降低的情况下，该融合网络利用极化图像技术和图像特征的机器学习的优势显著提高了分类准确性，展示了将极化图像技术整合到当前图像密集型数字病理诊断中的潜力。