影像组学中特征选择方法的应用与进展

Research progress and applications of feature selection methods in radiomics

影像组学是从医学影像中提取定量信息并对其进行表征和分析的技术,能够为临床疾病的诊疗提供补充性信息。特征选择在影像组学中发挥关键作用,能提高机器学习模型的精度和预测效果。现就影像组学中的特征选择方法分类、优缺点与应用,以及影响特征选择精度和稳定性的因素进行综述。

Radiomics is a technique that extracts quantitative information from medical images for characterization and analysis,providing supplementary information for the diagnosis and treatment of clinical diseases.Feature selection plays a crucial role in radiomics by enhancing the accuracy and predictive performance of machine learning models.This paper reviews the classification,advantages,and disadvantages of feature selection methods in radiomics,their applications,and factors that influence the accuracy and stability of feature selection.