[Research progress of computer-aided diagnosis in cancer based on deep learning and medical imaging].

Journal: Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Published Date:

Abstract

The dramatically increasing high-resolution medical images provide a great deal of useful information for cancer diagnosis, and play an essential role in assisting radiologists by offering more objective decisions. In order to utilize the information accurately and efficiently, researchers are focusing on computer-aided diagnosis (CAD) in cancer imaging. In recent years, deep learning as a state-of-the-art machine learning technique has contributed to a great progress in this field. This review covers the reports about deep learning based CAD systems in cancer imaging. We found that deep learning has outperformed conventional machine learning techniques in both tumor segmentation and classification, and that the technique may bring about a breakthrough in CAD of cancer with great prospect in the future clinical practice.

Authors

  • Shihui Chen
    School of Biomedical Engineering, Shenzhen University Health Sciences Center, Shenzhen, Guangdong 518060, P.R.China.
  • Weixiang Liu
    School of Biomedical Engineering, Shenzhen University Health Sciences Center, Shenzhen, Guangdong 518060, P.R.China.
  • Jing Qin
    School of Nursing, The Hong Kong Polytechnic University, Hong Kong, China.
  • Liangliang Chen
    School of Biomedical Engineering, Shenzhen University Health Sciences Center, Shenzhen, Guangdong 518060, P.R.China.
  • Guo Bin
    School of Biomedical Engineering, Shenzhen University Health Sciences Center, Shenzhen, Guangdong 518060, P.R.China.
  • Yuxiang Zhou
    School of Biomedical Engineering, Shenzhen University Health Sciences Center, Shenzhen, Guangdong 518060, P.R.China.
  • Tianfu Wang
    School of Biomedical Engineering, Shenzhen University Health Sciences Center, Shenzhen, Guangdong 518060, P.R.China.
  • Bingsheng Huang
    School of Biomedical Engineering, Shenzhen University Health Sciences Center, Shenzhen, Guangdong 518060, P.R.China.

Keywords

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