Higher-Order 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 higher-order transfer learning scheme is proposed by involving three phases, i.e., semantic attribute-specific 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 (LIDC-IDRI) dataset. The experimental results suggest that our higher-order 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 first-order 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.