Frontiers of Data and Computing ›› 2019, Vol. 1 ›› Issue (2): 37-52.doi: 10.11871/jfdc.issn.2096-742X.2019.02.004

Special Issue: “人工智能”专刊

Previous Articles     Next Articles

Application of Deep Learning in Medical Imaging Analysis: A Survey

Yu Yizhou,Ma Jiechao*(),Shi Dejun,Zhou Zhen   

  1. Deepwise AI Lab, Beijing 100080, China
  • Received:2019-09-25 Online:2019-12-20 Published:2020-01-15
  • Contact: Ma Jiechao


[Objective] This paper reviews the recent progress of deep learning researches and applications in medical image analysis. [Coverage] Relevant papers were first retrieved by keyword search and then by citation screening. [Methods] Deep learning based on convolutional neural networks is briefly introduced. Then, we review the diagnostic performance of deep learning on medical images in recent years with respect to different types of diseases, such as stroke, pulmonary nodules and bone age estimation. [Results] Deep learning for medical image interpretation has demonstrated advantages in many aspects, including accuracy, speed, stability and scalability. Meanwhile, existing problems may hinder clinical adoption of deep learning, such as dependence on a large amount of labelled data, inconsistent labeling standards, poor generalizability and interpretability of deep learning methods. [Limitations] There may be omissions of the retrieved literature, and it is impossible to compare the performance of the same deep learning model across different diseases. [Conclusions] Powerful artificial intelligence can improve the efficiency and accuracy of image interpretations for radiologists, but artificial intelligence is not perfect. Before being widely adopted in medical image interpretation, deep learning methods need more verification in real applications.

Key words: deep learning, medical imaging, survey