用于胃癌组织病理学分类的图像描述符对比分析
A Comparative Analysis of Image Descriptors for Histopathological Classification of Gastric Cancer
AI总结：
针对胃癌病理诊断需求，本研究结合手工与深度图像特征、搭配浅层分类器，在GasHisSDB数据集上开展对比实验，无需微调即可实现93.4%的F1值，为胃癌组织病理图像分类提供了高性能的特征与分类器组合方案。
摘要：
胃癌是全球发病率第五、致死率第四的癌症，5年生存率仅约20%，预后极差。尽管针对其病理生物学已有大量研究，但预后预测能力仍显不足，且病理科医生工作负荷大、存在潜在诊断误差的问题也加剧了这一现状，因此自动化、精准的组织病理学诊断工具至关重要。本研究采用机器学习与深度学习技术，将组织病理学图像分为健康与癌变两类。研究在GasHisSDB数据集上结合手工特征与深度特征，搭配浅层学习分类器开展实验，在不使用微调策略的前提下，对各类特征与分类器组合区分正常/异常组织病理图像的鲁棒性与性能进行对比分析并给出洞见。采用RF分类器时，该方法的F1值可达93.4%，验证了其有效性。
英文摘要：
Gastric cancer ranks as the fifth most common and fourth most lethal cancer globally, with a dismal 5-year survival rate of approximately 20%. Despite extensive research on its pathobiology, the prognostic predictability remains inadequate, compounded by pathologists' high workload and potential diagnostic errors. Thus, automated, accurate histopathological diagnosis tools are crucial. This study employs Machine Learning and Deep Learning techniques to classify histopathological images into healthy and cancerous categories. Using handcrafted and deep features with shallow learning classifiers on the GasHisSDB dataset, we offer a comparative analysis and insights into the most robust and high-performing combinations of features and classifiers for distinguishing between normal and abnormal histopathological images without fine-tuning strategies. With the RF classifier, our approach can reach F1 of 93.4%, demonstrating its validity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
