Dictionary learning LASSO for feature selection with application to hepatocellular carcinoma grading using contrast enhanced magnetic resonance imaging - Lei, Lei; Du, Li-Xin; He, Ying-Long; Yuan, Jian-Peng; Wang, Pan; Ye, Bao-Lin; Wang, Cong; Hou, ZuJun (Frontiers in Oncology, 2023)
The successful use of machine learning (ML) for medical diagnostic purposes has prompted myriad applications in cancer image analysis.Particularly for hepatocellular carcinoma (HCC) grading, there has been a surge of interest in ML based selection of the discriminative features from high-dimensional magnetic resonance imaging (MRI) radiomics data.As one of the most commonly used ML based selection method, the least absolute shrinkage and selection operator (LASSO) has high discriminative power of the essential feature based on linear representation between input features and output labels. However, most LASSO methods directly explore the original training data rather than effectively exploiting the most informative features of radiomics data for HCC grading. To overcome this limitation, this study marks the first attempt to propose a feature selection method based on LASSO with dictionary learning, where a dictionary is learned from the training features, using the Fisher ratio to maximize the discriminative information in the feature. To assess the effectiveness, we compared the proposed feature selection approach with other 5 state-of-the-practice methods. Experimental results demonstrate substantial performance improvements with the proposed method.