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.

翻译：对肝细胞癌（HCC）与肝内胆管癌（ICC）进行分类是肝脏疾病患者治疗方案选择及预后评估中的关键环节，而传统组织病理学诊断在此背景下存在局限性。本研究提出一种新型偏振与影像组学特征融合网络，该网络将肝脏病理样本穆勒矩阵图像提取的偏振特征与对应病理图像的影像组学特征相结合，用于HCC与ICC的分类。我们的融合网络采用双层融合策略，包括早期特征级融合与晚期分类级融合。通过整合偏振成像技术和基于图像特征的机器学习优势，所提出的融合网络显著提升了分类精度。值得注意的是，即使在成像分辨率降低的情况下，由于偏振特征提供的额外信息（这些信息可能不符合人类视觉感知），该融合网络仍能保持稳健性能。实验结果充分证明了该融合网络作为HCC与ICC计算机辅助诊断工具的巨大潜力，展示了将偏振成像技术整合至当前图像密集型数字病理诊断中的优势与前景。我们致力于将此创新方法发表于顶级期刊，为医学影像与癌症诊断领域提供新颖见解与实用工具。通过将偏振成像引入肝癌分类，本研究展示了其在解决医学图像分析挑战中的跨学科潜力，有望推动医学影像与癌症诊断技术的进步。