Zou Lian, Yu Shaode, Meng Tiebao, Zhang Zhicheng, Liang Xiaokun, Xie Yaoqin
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China.
Comput Math Methods Med. 2019 Mar 25;2019:6509357. doi: 10.1155/2019/6509357. eCollection 2019.
This study reviews the technique of convolutional neural network (CNN) applied in a specific field of mammographic breast cancer diagnosis (MBCD). It aims to provide several clues on how to use CNN for related tasks. MBCD is a long-standing problem, and massive computer-aided diagnosis models have been proposed. The models of CNN-based MBCD can be broadly categorized into three groups. One is to design shallow or to modify existing models to decrease the time cost as well as the number of instances for training; another is to make the best use of a pretrained CNN by transfer learning and fine-tuning; the third is to take advantage of CNN models for feature extraction, and the differentiation of malignant lesions from benign ones is fulfilled by using machine learning classifiers. This study enrolls peer-reviewed journal publications and presents technical details and pros and cons of each model. Furthermore, the findings, challenges and limitations are summarized and some clues on the future work are also given. Conclusively, CNN-based MBCD is at its early stage, and there is still a long way ahead in achieving the ultimate goal of using deep learning tools to facilitate clinical practice. This review benefits scientific researchers, industrial engineers, and those who are devoted to intelligent cancer diagnosis.
本研究回顾了卷积神经网络（CNN）应用于乳腺钼靶乳腺癌诊断（MBCD）特定领域的技术。其目的是提供一些关于如何将CNN用于相关任务的线索。MBCD是一个长期存在的问题，并且已经提出了大量的计算机辅助诊断模型。基于CNN的MBCD模型大致可分为三类。一类是设计浅层模型或修改现有模型以降低时间成本以及训练所需的实例数量；另一类是通过迁移学习和微调充分利用预训练的CNN；第三类是利用CNN模型进行特征提取，并通过使用机器学习分类器实现恶性病变与良性病变的区分。本研究收录了经过同行评审的期刊出版物，并介绍了每个模型的技术细节以及优缺点。此外，总结了研究结果、挑战和局限性，并给出了一些关于未来工作的线索。总之，基于CNN的MBCD尚处于早期阶段，在实现使用深度学习工具促进临床实践这一最终目标方面仍有很长的路要走。本综述对科研人员、工业工程师以及致力于智能癌症诊断的人员有益。
