Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, India.
Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, India.
J Med Imaging Radiat Sci. 2020 Mar;51(1):182-193. doi: 10.1016/j.jmir.2019.11.001. Epub 2019 Dec 26.
Histopathology is a method used for breast cancer diagnosis. Machine learning (ML) methods have achieved success for supervised learning tasks in the medical domain. In this article, we investigate the impact of ML for the diagnosis of breast cancer using histopathology images of conventional photomicroscopy. Cancer diagnosis is the identification of images as cancer or noncancer, and this involves image preprocessing, feature extraction, classification, and performance analysis. In this article, different approaches to perform these necessary steps are reviewed. We find that most ML research for breast cancer diagnosis has been focused on deep learning. Based on inferences from the recent research activities, we discuss how ML methods can benefit conventional microscopy-based breast cancer diagnosis. Finally, we discuss the research gaps of ML approaches for the implementation in a real pathology environment and propose future research guidelines.
组织病理学是用于乳腺癌诊断的一种方法。机器学习（ML）方法在医学领域的监督学习任务中取得了成功。在本文中，我们研究了使用常规显微镜的组织病理学图像对乳腺癌进行 ML 诊断的影响。癌症诊断是将图像识别为癌症或非癌症，这涉及图像预处理、特征提取、分类和性能分析。在本文中，我们回顾了执行这些必要步骤的不同方法。我们发现，大多数用于乳腺癌诊断的 ML 研究都集中在深度学习上。基于对最近研究活动的推断，我们讨论了 ML 方法如何有益于基于常规显微镜的乳腺癌诊断。最后，我们讨论了 ML 方法在实际病理环境中的实施的研究差距，并提出了未来的研究指南。
