Multicenter ComputerAided Diagnosis for Lymph Nodes Using Unsupervised DomainAdaptation Networks Based on CrossDomain Confounding Representations To achieve the robust highperformance computeraided diagnosis systems for lymph nodes, CT images may be typically collected from multicenter data, which cause the isolated performance of the model based on different data source centers. The variability adaptation problem of lymph node data which is related to the problem of domain adaptation in deep learning differs from the general domain adaptation problem because of the typically larger CT image size and more complex data distributions. Therefore, domain adaptation for this problem needs to consider the shared feature representation and even the conditioning information of each domain so that the adaptation network can capture significant discriminative representations in a domaininvariant space. This paper extracts domaininvariant features based on a crossdomain confounding representation and proposes a cycleconsistency learning framework to encourage the network to preserve classconditioning information through crossdomain image translations. Compared with the performance of different domain adaptation methods, the accurate rate of our method achieves at least 4.4% points higher under multicenter lymph node data. The pixellevel crossdomain image mapping and the semanticlevel cycle consistency provided a stable confounding representation with classconditioning information to achieve effective domain adaptation under complex feature distribution.

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.