  Although deep learning models like CNNs have achieved great success in
medical image analysis, the small size of medical datasets remains a major
bottleneck in this area. To address this problem, researchers have started
looking for external information beyond current available medical datasets.
Traditional approaches generally leverage the information from natural images
via transfer learning. More recent works utilize the domain knowledge from
medical doctors, to create networks that resemble how medical doctors are
trained, mimic their diagnostic patterns, or focus on the features or areas
they pay particular attention to. In this survey, we summarize the current
progress on integrating medical domain knowledge into deep learning models for
various tasks, such as disease diagnosis, lesion, organ and abnormality
detection, lesion and organ segmentation. For each task, we systematically
categorize different kinds of medical domain knowledge that have been utilized
and their corresponding integrating methods. We also provide current challenges
and directions for future research.
