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.

中文翻译：

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深度学习用于异构医学数据分析

当前，如何利用海量医学信息资源为疾病的诊断和治疗提供科学的决策，总结各种治疗方案的疗效，更好地为决策​​管理，医疗和科学研究提供服务，引起了越来越多研究者的关注。深度学习作为学术界和业界最关注的焦点，已经在许多领域得到有效应用，并且胜过大多数机器学习方法。在这种背景下，基于深度学习的医学数据分析应运而生。在此调查中，我们着重于对当前的发展进行回顾和分类。首先，我们充分讨论异构医学数据的范围，特征和结构。之后，主要是 医学数据分析涉及的主要深度学习模型，包括它们的变体和各种混合模型，以及医学数据分析的主要任务，都分别通过一系列典型案例进行了分析和审查。最后，我们简要介绍了深度学习开发工具的某些有用的在线资源。