DDeep3M: Docker-powered deep learning for biomedical image segmentation.

Journal: Journal of neuroscience methods
Published Date: Jun 18, 2020

Abstract

BACKGROUND: Deep learning models are turning out to be increasingly popular in biomedical image processing. The fruitful utilization of these models, in most cases, is substantially restricted by the complicated configuration of computational environments, resulting in the noteworthy increment of the time and endeavors to reproduce the outcomes of the models.

Authors

  • Xinglong Wu
    School of Computer Science & Engineering, Wuhan Institute of Technology, Wuhan, Hubei, 430205, China; Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
  • Shangbin Chen
    Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China. Electronic address: sbchen@hust.edu.cn.
  • Jin Huang
    College of Life Science, Yangtze University, Jingzhou, Hubei 434023, P. R. China; Institute of Agricultural Products Processing, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, PR China.
  • Anan Li
    Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.
  • Rong Xiao
    Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
  • Xinwu Cui
    Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China.

Keywords

External Resources

Popular Topics
Recent Journals