Transfer learning privileged information fuels CAD diagnosis of breast cancer The efficiency in breast cancer from imagingbased computeraided diagnosis (CAD) has been revealed in recent years. As a fact, the methods grounded on a single modality constantly lack behind multimodal CAD imaging. However, owing to the restrictions of imaging devices, expressly in rural hospitals, singlemodal imaging becomes a favorite in clinical practice for diagnosis. A fresh learning model trending nowadays known as learning using privileged information (LUPI) adopts additional privileged information (PI) modality to help during the training stage, but PI does not contribute in the testing stage. Meanwhile, the link exists between PI and training samples; the same is then reassigned to the learned model. We propose a LUPIbased CAD framework for breast cancer using privileged information in this work. The work offers both a classifier or featurelevel LUPI, in which the information is shifted from the additional PI modality to the diagnosis modality. A thorough comparison has been made among six classifierlevel algorithms and six featurelevel LUPI algorithms. The experimental results on both the acquired primary datasets show that all classifierlevel and deep learningbased featurelevel LUPI algorithms can enhance the performance of a singlemodal imagingbased CAD for breast cancer by relocating PI.

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.