  Recent advances in deep learning have resulted in great successes in various
applications. Although semi-supervised or unsupervised learning methods have
been widely investigated, the performance of deep neural networks highly
depends on the annotated data. The problem is that the budget for annotation is
usually limited due to the annotation time and expensive annotation cost in
medical data. Active learning is one of the solutions to this problem where an
active learner is designed to indicate which samples need to be annotated to
effectively train a target model. In this paper, we propose a novel active
learning method, confident coreset, which considers both uncertainty and
distribution for effectively selecting informative samples. By comparative
experiments on two medical image analysis tasks, we show that our method
outperforms other active learning methods.
