Hyperparameter selection for dataset-constrained semantic segmentation: Practical machine learning optimization.

Journal: Journal of applied clinical medical physics
PMID: 39387832

Abstract

PURPOSE/AIM: This paper provides a pedagogical example for systematic machine learning optimization in small dataset image segmentation, emphasizing hyperparameter selections. A simple process is presented for medical physicists to examine hyperparameter optimization. This is also applied to a case-study, demonstrating the benefit of the method.

Authors

  • Chris Boyd
    Allied Health and Human Performance, University of South Australia, Adelaide, Australia.
  • Gregory C Brown
    Allied Health and Human Performance, University of South Australia, Adelaide, Australia.
  • Timothy J Kleinig
    Department of Neurology, Royal Adelaide Hospital, Adelaide, Australia.
  • Wolfgang Mayer
    Discipline of Surgery, University of Adelaide, Adelaide, Australia.
  • Joseph Dawson
    Department of Vascular and Endovascular Surgery, Royal Adelaide Hospital, Adelaide, Australia.
  • Mark Jenkinson
    Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK.
  • Eva Bezak
    Cancer Research Institute and School of Health Sciences, University of South Australia, GPO BOX 2471, Adelaide, SA, 5001, Australia.

Keywords

External Resources

Popular Topics
Recent Journals