Automatic medical imaging segmentation via self-supervising large-scale convolutional neural networks.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Published Date: Jan 9, 2025

Abstract

PURPOSE: This study aims to develop a robust, large-scale deep learning model for medical image segmentation, leveraging self-supervised learning to overcome the limitations of supervised learning and data variability in clinical settings.

Authors

  • Yuheng Li
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA.
  • Jacob F Wynne
    Department of Radiation Oncology, Emory University, Atlanta, GA, USA.
  • Yizhou Wu
    School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.
  • Richard L J Qiu
    Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA, United States of America.
  • Sibo Tian
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA.
  • Tonghe Wang
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322.
  • Pretesh R Patel
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA.
  • David S Yu
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA.
  • Xiaofeng Yang
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA.

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