Department of Software Engineering, University of Engineering and Technology Taxila, Taxila, 47050, Pakistan.
Department of Computer Engineering, University of Engineering and Technology Taxila, Taxila, 47050, Pakistan.
J Med Syst. 2018 Oct 8;42(11):226. doi: 10.1007/s10916-018-1088-1.
The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an affective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering have made medical image analysis one of the top research and development area. One of the reasons for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This includes application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. In this study, a comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented. The challenges and potential of these techniques are also highlighted.
通过分析临床实践中生成的图像来解决临床问题的科学被称为医学图像分析。其目的是以更有效的方式提取信息，从而改善临床诊断。生物医学工程领域的最新进展使得医学图像分析成为最热门的研究和开发领域之一。推动这一进展的原因之一是将机器学习技术应用于医学图像分析。深度学习成功地用作机器学习的工具，其中神经网络能够自动学习特征。这与传统上使用手工制作特征的方法形成对比。选择和计算这些特征是一项具有挑战性的任务。在深度学习技术中，深度卷积网络被积极用于医学图像分析。这包括分割、异常检测、疾病分类、计算机辅助诊断和检索等应用领域。在这项研究中，对使用深度卷积网络进行医学图像分析的最新技术进行了全面的回顾。还强调了这些技术的挑战和潜力。
