A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis
It provides a systematic review for researchers in medical image analysis, but it is incremental as it surveys existing approaches without introducing new methods.
This survey addresses the challenge of small medical datasets in deep learning by summarizing methods to incorporate medical domain knowledge, such as mimicking doctor training or diagnostic patterns, into models for tasks like disease diagnosis and segmentation.
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.