Technical note: Progressive deep learning: An accelerated training strategy for medical image segmentation.

Journal: Medical physics
Published Date:

Abstract

BACKGROUND: Recent advancements in Deep Learning (DL) methodologies have led to state-of-the-art performance in a wide range of applications especially in object recognition, classification, and segmentation of medical images. However, training modern DL models requires a large amount of computation and long training times due to the complex nature of network structures and the large number of training datasets involved. Moreover, it is an intensive, repetitive manual process to select the optimized configuration of hyperparameters for a given DL network.

Authors

  • Byongsu Choi
    Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea.
  • Sven Olberg
    Department of Radiation Oncology, Washington University in St. Louis, St. Louis, MO, 63110, USA.
  • Justin C Park
    Department of Radiation Oncology, Washington University in St. Louis, St. Louis, MO, 63110, USA.
  • Jin Sung Kim
    Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea.
  • Deepak K Shrestha
    Department of Radiation Oncology, Mayo Clinic, Jacksonville, Florida, USA.
  • Shridhar Yaddanapudi
    Department of Radiation Oncology, Mayo Clinic, Jacksonville, Florida, USA.
  • Keith M Furutani
    Department of Radiation Oncology, Mayo Clinic, Jacksonville, Florida, USA.
  • Chris J Beltran
    Department of Radiation Oncology, Mayo Clinic, Jacksonville, Florida, USA.