A novel adaptive cubic quasi-Newton optimizer for deep learning based medical image analysis tasks, validated on detection of COVID-19 and segmentation for COVID-19 lung infection, liver tumor, and optic disc/cup.

Journal: Medical physics
Published Date: Oct 6, 2022

Abstract

BACKGROUND: Most of existing deep learning research in medical image analysis is focused on networks with stronger performance. These networks have achieved success, while their architectures are complex and even contain massive parameters ranging from thousands to millions in numbers. The nature of high dimension and nonconvex makes it easy to train a suboptimal model through the popular stochastic first-order optimizers, which only use gradient information.

Authors

  • Yan Liu
    Department of Clinical Microbiology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, 200072, People's Republic of China.
  • Maojun Zhang
    College of System Engineering, National University of Defense Technology, Changsha 410073, China.
  • Zhiwei Zhong
    College of Systems Engineering, National University of Defense Technology, Changsha, China.
  • XiangRong Zeng
    College of Systems Engineering, National University of Defense Technology, Changsha 410073, China.

Keywords

External Resources

Popular Topics
Recent Journals