Medical imaging diagnosis is mostly subjective, as it depends on medical experts. Hence, the service provided is limited by expert opinion variations and image complexity as well. However, with the increasing advancements in deep learning field, techniques are developed to help in the diagnosis and risk assessment processes. In this article, we survey different types of images in healthcare. A review of the concept and research methodology of Radiomics will highlight the potentials of integrated diagnostics. Convolutional neural networks can play an important role in next generations of automated imaging biomarker extraction and big data analytics systems. Examples are provided of what is already feasible today and also describe additional technological components required for successful clinical implementation.

中文翻译：

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空间和时间中的图像

医学影像诊断主要是主观的，因为它取决于医学专家。因此，所提供的服务也受到专家意见变化和图像复杂性的限制。然而，随着深度学习领域的不断进步，人们开发了一些技术来帮助诊断和风险评估过程。在本文中，我们调查了医疗保健中不同类型的图像。回顾放射组学的概念和研究方法将突出综合诊断的潜力。卷积神经网络可以在下一代自动成像生物标志物提取和大数据分析系统中发挥重要作用。提供了当今已经可行的示例，并描述了成功临床实施所需的其他技术组件。