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Deep Learning in Radiology

机译：放射学深入学习

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As radiology is inherently a data-driven specialty, it is especially conducive to utilizing data processing techniques. One such technique, deep learning (DL), has become a remarkably powerful tool for image processing in recent years. In this work, the Association of University Radiologists Radiology Research Alliance Task Force on Deep Learning provides an overview of DL for the radiologist. This article aims to present an overview of DL in a manner that is understandable to radiologists; to examine past, present, and future applications; as well as to evaluate how radiologists may benefit from this remarkable new tool. We describe several areas within radiology in which DL techniques are having the most significant impact: lesion or disease detection, classification, quantification, and segmentation. The legal and ethical hurdles to implementation are also discussed. By taking advantage of this powerful tool, radiologists can become increasingly more accurate in their interpretations with fewer errors and spend more time to focus on patient care.
机译：由于放射学固有地是数据驱动的专业，因此特别有利于利用数据处理技术。一种这样的技术，深度学习（DL），近年来已经成为图像处理的一个非常强大的工具。在这项工作中，大学放射科学家放射学研究联盟工作队的深度学习协会提供了放射科医师DL的概述。本文旨在以一种可理解的放射科医师可以展示DL的概述;检查过去，现在和未来的应用;除了评估放射科医生可以从这种非凡的新工具中受益。我们描述了放射科内的几个区域，其中DL技术具有最显着的影响：病变或疾病检测，分类，量化和分割。还讨论了实施的法律和道德障碍。通过利用这种强大的工具，放射科学家在他们的解释中可以越来越准确，错误的错误，并花更多时间专注于患者护理。

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