University of Miami, Department of Electrical and Computer Engineering, Memorial Dr, Coral Gables, FL, 33146, USA.
Umm Al-Qura University, Department of Computer Science, Alawali, Mecca, 24381, Saudi Arabia.
Comput Biol Med. 2021 Apr;131:104248. doi: 10.1016/j.compbiomed.2021.104248. Epub 2021 Feb 9.
Despite its proven record as a breast cancer screening tool, mammography remains labor-intensive and has recognized limitations, including low sensitivity in women with dense breast tissue. In the last ten years, Neural Network advances have been applied to mammography to help radiologists increase their efficiency and accuracy. This survey aims to present, in an organized and structured manner, the current knowledge base of convolutional neural networks (CNNs) in mammography. The survey first discusses traditional Computer Assisted Detection (CAD) and more recently developed CNN-based models for computer vision in mammography. It then presents and discusses the literature on available mammography training datasets. The survey then presents and discusses current literature on CNNs for four distinct mammography tasks: (1) breast density classification, (2) breast asymmetry detection and classification, (3) calcification detection and classification, and (4) mass detection and classification, including presenting and comparing the reported quantitative results for each task and the pros and cons of the different CNN-based approaches. Then, it offers real-world applications of CNN CAD algorithms by discussing current Food and Drug Administration (FDA) approved models. Finally, this survey highlights the potential opportunities for future work in this field. The material presented and discussed in this survey could serve as a road map for developing CNN-based solutions to improve mammographic detection of breast cancer further.
尽管乳腺 X 线摄影术已被证实可作为乳腺癌筛查工具，但它仍然需要大量的人力，并且存在公认的局限性，包括在乳腺组织致密的女性中敏感性较低。在过去的十年中，神经网络技术已被应用于乳腺 X 线摄影术，以帮助放射科医生提高效率和准确性。本调查旨在以有组织和结构化的方式呈现乳腺 X 线摄影术卷积神经网络 (CNN) 的当前知识库。该调查首先讨论了传统的计算机辅助检测 (CAD) 和最近开发的基于 CNN 的计算机视觉模型在乳腺 X 线摄影术中的应用。然后介绍并讨论了现有的乳腺 X 线摄影术训练数据集的文献。本调查接着介绍并讨论了目前关于 CNN 的四项不同的乳腺 X 线摄影术任务的文献：（1）乳腺密度分类，（2）乳腺不对称性检测和分类，（3）钙化检测和分类，以及（4）肿块检测和分类，包括为每个任务呈现和比较报告的定量结果，以及不同基于 CNN 的方法的优缺点。然后，通过讨论当前获得美国食品和药物管理局 (FDA) 批准的模型，介绍了 CNN CAD 算法的实际应用。最后，本调查强调了该领域未来工作的潜在机会。本调查中介绍和讨论的材料可以作为开发基于 CNN 的解决方案以进一步提高乳腺 X 线摄影术乳腺癌检测的路线图。
