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计算机辅助乳腺癌检测在乳腺 X 线片中的应用:综述。

Computer-aided breast cancer detection using mammograms: a review.

机构信息

Department of Electrical and Computer Engineering, Ngee Ann Polytechnic, 599489 Singapore.

出版信息

IEEE Rev Biomed Eng. 2013;6:77-98. doi: 10.1109/RBME.2012.2232289. Epub 2012 Dec 11.

Abstract

The American Cancer Society (ACS) recommends women aged 40 and above to have a mammogram every year and calls it a gold standard for breast cancer detection. Early detection of breast cancer can improve survival rates to a great extent. Inter-observer and intra-observer errors occur frequently in analysis of medical images, given the high variability between interpretations of different radiologists. Also, the sensitivity of mammographic screening varies with image quality and expertise of the radiologist. So, there is no golden standard for the screening process. To offset this variability and to standardize the diagnostic procedures, efforts are being made to develop automated techniques for diagnosis and grading of breast cancer images. A few papers have documented the general trend of computer-aided diagnosis of breast cancer, making a broad study of the several techniques involved. But, there is no definitive documentation focusing on the mathematical techniques used in breast cancer detection. This review aims at providing an overview about recent advances and developments in the field of Computer-Aided Diagnosis (CAD) of breast cancer using mammograms, specifically focusing on the mathematical aspects of the same, aiming to act as a mathematical primer for intermediates and experts in the field.

摘要

美国癌症协会(ACS)建议 40 岁及以上的女性每年进行一次乳房 X 光检查,并将其称为乳腺癌检测的“金标准”。乳腺癌的早期检测在很大程度上可以提高生存率。由于不同放射科医生的解释存在高度差异,医学图像分析中经常会出现观察者间和观察者内误差。此外,乳房 X 光筛查的敏感性会随着图像质量和放射科医生的专业水平而变化。因此,筛查过程没有“金标准”。为了弥补这种可变性并使诊断程序标准化,正在努力开发用于乳腺癌图像诊断和分级的自动化技术。有几篇论文记录了计算机辅助乳腺癌诊断的总体趋势,对所涉及的几种技术进行了广泛研究。但是,没有专门针对用于乳腺癌检测的数学技术的明确文档。本综述旨在概述使用乳房 X 光片进行计算机辅助诊断(CAD)乳腺癌领域的最新进展和发展,特别关注其数学方面,旨在为该领域的中级和专家提供数学入门知识。

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