论文标题
医学成像中深度学习的概述
An overview of deep learning in medical imaging
论文作者
论文摘要
在最近的十年中，机器学习（ML）已经考虑了巨大的考虑。这一成功始于2012年，当时ML模型在世界上最著名的计算机视觉竞争中取得了非凡的胜利。该模型是一种称为深度学习（DL）的卷积神经系统（CNN）。从那时起，研究人员开始有效地参与DL最快的研究领域。如今，DL系统是跨越广泛学科的尖端ML系统，从人类语言处理到视频分析，并且通常在学术界和企业领域中使用。最近的进步可以为医疗领域带来巨大的进步。改进和创新的方法用于数据处理，图像分析，并可以逐渐改善诊断技术和药用服务。已经提供了在用于医学成像的DL领域中具有相关问题的当前发展的快速审查。 The primary purposes of the review are four: (i) provide a brief prolog to DL by discussing different DL models, (ii) review of the DL usage for medical image analysis (classification, detection, segmentation, and registration), (iii) review seven main application fields of DL in medical imaging, (iv) give an initial stage to those keen on adding to the research area about DL in clinical imaging by providing links of some useful informative assets,例如免费可用的DL代码，公共数据集表7和医学成像竞赛来源表8，并通过概述了医学科学领域的独特的连续困难，经验教训和DL的未来，结束了我们的调查。
Machine learning (ML) has seen enormous consideration during the most recent decade. This success started in 2012 when an ML model accomplished a remarkable triumph in the ImageNet Classification, the world's most famous competition for computer vision. This model was a kind of convolutional neural system (CNN) called deep learning (DL). Since then, researchers have started to participate efficiently in DL's fastest developing area of research. These days, DL systems are cutting-edge ML systems spanning a broad range of disciplines, from human language processing to video analysis, and commonly used in the scholarly world and enterprise sector. Recent advances can bring tremendous improvement to the medical field. Improved and innovative methods for data processing, image analysis and can significantly improve the diagnostic technologies and medicinal services gradually. A quick review of current developments with relevant problems in the field of DL used for medical imaging has been provided. The primary purposes of the review are four: (i) provide a brief prolog to DL by discussing different DL models, (ii) review of the DL usage for medical image analysis (classification, detection, segmentation, and registration), (iii) review seven main application fields of DL in medical imaging, (iv) give an initial stage to those keen on adding to the research area about DL in clinical imaging by providing links of some useful informative assets, such as freely available DL codes, public datasets Table 7, and medical imaging competition sources Table 8 and end our survey by outlining distinct continuous difficulties, lessons learned and future of DL in the field of medical science.
