Gardezi Syed Jamal Safdar, Elazab Ahmed, Lei Baiying, Wang Tianfu
National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong, Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
J Med Internet Res. 2019 Jul 26;21(7):e14464. doi: 10.2196/14464.
Machine learning (ML) has become a vital part of medical imaging research. ML methods have evolved over the years from manual seeded inputs to automatic initializations. The advancements in the field of ML have led to more intelligent and self-reliant computer-aided diagnosis (CAD) systems, as the learning ability of ML methods has been constantly improving. More and more automated methods are emerging with deep feature learning and representations. Recent advancements of ML with deeper and extensive representation approaches, commonly known as deep learning (DL) approaches, have made a very significant impact on improving the diagnostics capabilities of the CAD systems.
This review aimed to survey both traditional ML and DL literature with particular application for breast cancer diagnosis. The review also provided a brief insight into some well-known DL networks.
In this paper, we present an overview of ML and DL techniques with particular application for breast cancer. Specifically, we search the PubMed, Google Scholar, MEDLINE, ScienceDirect, Springer, and Web of Science databases and retrieve the studies in DL for the past 5 years that have used multiview mammogram datasets.
The analysis of traditional ML reveals the limited usage of the methods, whereas the DL methods have great potential for implementation in clinical analysis and improve the diagnostic capability of existing CAD systems.
From the literature, it can be found that heterogeneous breast densities make masses more challenging to detect and classify compared with calcifications. The traditional ML methods present confined approaches limited to either particular density type or datasets. Although the DL methods show promising improvements in breast cancer diagnosis, there are still issues of data scarcity and computational cost, which have been overcome to a significant extent by applying data augmentation and improved computational power of DL algorithms.
机器学习（ML）已成为医学影像研究的重要组成部分。多年来，ML方法已从手动种子输入发展到自动初始化。随着ML方法的学习能力不断提高，ML领域的进步催生了更智能、更自主的计算机辅助诊断（CAD）系统。越来越多的自动化方法借助深度特征学习和表示方式不断涌现。ML与更深层次、更广泛的表示方法（通常称为深度学习（DL）方法）的最新进展，对提高CAD系统的诊断能力产生了非常重大的影响。
本综述旨在调查传统ML和DL文献在乳腺癌诊断中的具体应用。该综述还简要介绍了一些知名的DL网络。
在本文中，我们概述了ML和DL技术在乳腺癌中的具体应用。具体而言，我们搜索了PubMed、谷歌学术、MEDLINE、ScienceDirect、Springer和Web of Science数据库，并检索了过去5年中使用多视图乳腺X线摄影数据集的DL研究。
对传统ML的分析表明这些方法的使用有限，而DL方法在临床分析中具有巨大的实施潜力，并能提高现有CAD系统的诊断能力。
从文献中可以发现，与钙化相比，乳腺密度的异质性使肿块的检测和分类更具挑战性。传统的ML方法呈现出局限于特定密度类型或数据集的有限方法。尽管DL方法在乳腺癌诊断方面显示出有希望的改进，但仍然存在数据稀缺和计算成本的问题，通过应用数据增强和改进DL算法的计算能力，这些问题已在很大程度上得到克服。
