Predicting the Semantic Characteristics of Pulmonary Nodules using Feature Selection Based on Maximumrelevance Minimumredundancy Computeraided diagnosis (CAD) is mainly used in disease diagnosis and cause analysis. For example, using CAD to make early predictions of the semantic features of lung nodules is critical for helping physicians judge the semantic features of solitary pulmonary nodules. It is an effective method to predict disease using the features calculated from CT images. But how to select the most relevant features from the large number of image features is still a challenge. In this paper, we perform feature selection using maximumrelevance minimumredundancy criteria based on applying a support vector machine (MRMR_SVM) on four types of computed image features to predict the semantic characteristics of pulmonary nodules over seven categories. The proposed method has the following advantages. 1) It improves work efficiency and reduces costs compared to manual evaluation by radiologists. 2) It combines a few key image features with specific semantic features to provide a basis for radiologistsu0027 diagnosis. 3) It eliminates noisy data and improves accuracy of early prediction of pulmonary nodules compared to using all features. The experimental results show that the proposed method performs well at predicting the semantic features of lung nodules in terms of accuracy and running time.

Higherorder Transfer Learning for Pulmonary Nodule Attribute Prediction in Chest CT Images Attributes like texture, lobulation, malignancy, etc., are commonly used to describe the phenotype of a pulmonary nodule in computed tomography (CT) image, which can provide useful medical knowledge for the identification of early stage lung cancer. There may exist certain relations among these attributes, and some attributes may naturally imply or boost others that have been less comprehensively exploited in previous studies. In this paper, we explicitly model the relations among 11 attributes of nodules by way of transfer learning and extract a metastructure that captures the transferabilities across deep features of these attributes. Specifically, a higherorder transfer learning scheme is proposed by involving three phases, i.e., semantic attributespecific modeling, semantic attributes transfer modeling and pathologic attribute generalizing, to explore the strongest association across various attributes and to boost the nodule attribute predictions in chest CT images. The proposed approach has been evaluated on the 2632 nodules in the public Lung Image Database Consortium and Image Database Resource Initiative (LIDCIDRI) dataset. The experimental results suggest that our higherorder transfer approach shows the superior predictive performance not only in the most of the semantic attributes compared with the schemes of learning from scratch and the firstorder transfer but also for the pathologic attribute compared with the related studies. In addition, we demonstrate an attribute transfer graph to reveal which attributes combination can supply the most useful information to boost the predictive performance of target attributes.