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.

Breast Cancer Classification using Deep Transfer Learning on Structured Healthcare Data Efforts to improve early identification of aggressive high grade breast cancers, which pose the greatest risk to patient health if not detected early, are hindered by the rarity of these events. To address this problem, we proposed an accurate and efficient deep transfer learning method to handle the imbalanced data problem that is prominent in breast cancer data. In contrast to existing approaches based primarily on large image databases, we focused on structured data, which has not been commonly used for deep transfer learning. We used a number of publicly available breast cancer data sets to generate a pretrained model and transfer learned concepts to predict high grade malignant tumors in patients diagnosed with breast cancer at Mayo Clinic. We compared our results with stateoftheart techniques for addressing the problem of imbalanced learning and confirmed the superiority of the proposed method. To further demonstrate the ability of the proposed method to handle different degrees of class imbalance, a series of experiments were performed on publicly available breast cancer data under simulated class imbalanced settings. Based on the experimental results, we concluded that the proposed deep transfer learning on structured data can be used as an efficient method to handle imbalanced class problems in clinical research.