  Deep learning has received extensive research interest in developing new
medical image processing algorithms, and deep learning based models have been
remarkably successful in a variety of medical imaging tasks to support disease
detection and diagnosis. Despite the success, the further improvement of deep
learning models in medical image analysis is majorly bottlenecked by the lack
of large-sized and well-annotated datasets. In the past five years, many
studies have focused on addressing this challenge. In this paper, we reviewed
and summarized these recent studies to provide a comprehensive overview of
applying deep learning methods in various medical image analysis tasks.
Especially, we emphasize the latest progress and contributions of
state-of-the-art unsupervised and semi-supervised deep learning in medical
image analysis, which are summarized based on different application scenarios,
including classification, segmentation, detection, and image registration. We
also discuss the major technical challenges and suggest the possible solutions
in future research efforts.
