Deep learning (DL)-based interpretation of medical images has reached a critical juncture of expanding outside research projects into translational ones, and is ready to make its way to the clinics. Advances over the last decade in data availability, DL techniques, as well as computing capabilities have accelerated this journey. Through this journey, today we have a better understanding of the challenges to and pitfalls of wider adoption of DL into clinical care, which, according to us, should and will drive the advances in this field in the next few years. The most important among these challenges are the lack of an appropriately digitized environment within healthcare institutions, the lack of adequate open and representative datasets on which DL algorithms can be trained and tested, and the lack of robustness of widely used DL training algorithms to certain pervasive pathological characteristics of medical images and repositories. In this review, we provide an overview of the role of imaging in oncology, the different techniques that are shaping the way DL algorithms are being made ready for clinical use, and also the problems that DL techniques still need to address before DL can find a home in clinics. Finally, we also provide a summary of how DL can potentially drive the adoption of digital pathology, vendor neutral archives, and picture archival and communication systems. We caution that the respective researchers may find the coverage of their own fields to be at a high-level. This is so by design as this format is meant to only introduce those looking in from outside of deep learning and medical research, respectively, to gain an appreciation for the main concerns and limitations of these two fields instead of telling them something new about their own.

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2021 年使用深度学习进行癌症图像分析的更新

基于深度学习 (DL) 的医学图像解读已经到了将外部研究项目扩展到转化项目的关键时刻，并准备进入临床。过去十年在数据可用性、深度学习技术以及计算能力方面的进步加速了这一旅程。通过这次旅程，今天我们更好地了解了将深度学习更广泛地应用于临床护理的挑战和陷阱，据我们认为，这应该并且将在未来几年推动该领域的进步。这些挑战中最重要的是医疗机构内缺乏适当的数字化环境，缺乏足够的开放和具有代表性的数据集来训练和测试 DL 算法，广泛使用的 DL 训练算法对医学图像和存储库的某些普遍病理特征缺乏鲁棒性。在这篇综述中，我们概述了成像在肿瘤学中的作用、正在塑造 DL 算法为临床使用做好准备的方式的不同技术，以及在 DL 找到合适的方法之前，DL 技术仍然需要解决的问题。家在诊所。最后，我们还总结了 DL 如何潜在地推动数字病理学、供应商中立档案以及图片档案和通信系统的采用。我们警告说，各自的研究人员可能会发现他们自己领域的覆盖率处于较高水平。这是设计使然，因为这种格式旨在分别介绍那些从深度学习和医学研究之外看的人，