Deep learning for heterogeneous medical data analysis

At present, how to make use of massive medical information resources to provide scientific decision-making for the diagnosis and treatment of diseases, summarize the curative effect of various treatment schemes, and better serve the decision-making management, medical treatment, and scientific research, has drawn more and more attention of researchers. Deep learning, as the focus of most concern by both academia and industry, has been effectively applied in many fields and has outperformed most of the machine learning methods. Under this background, deep learning based medical data analysis emerged. In this survey, we focus on reviewing and then categorizing the current development. Firstly, we fully discuss the scope, characteristic and structure of the heterogeneous medical data. Afterward and primarily, the main deep learning models involved in medical data analysis, including their variants and various hybrid models, as well as main tasks in medical data analysis are all analyzed and reviewed in a series of typical cases respectively. Finally, we provide a brief introduction to certain useful online resources of deep learning development tools.