On combining active and transfer learning for medical data classification

This study presents a novel algorithm which combines active learning (AL) and transfer learning for medical data classification. The main idea of the proposed algorithm is iteratively querying a small number of informative unlabelled target samples, and, at the same time, removing the source samples which do not fit with the posterior probability distributions in the target domain, so as to combine the basic idea of AL with transfer learning. The experimental results obtained in the classification of the datasets from the University of California Irvine (UCI) Machine Learning Repository and The Cancer Imaging Archive (TCIA) confirm the effectiveness of the proposed algorithm.