Federated Learning for Renal Tumor Segmentation and Classification on Multi-Center MRI Dataset.

Journal: Journal of magnetic resonance imaging : JMRI
Published Date:

Abstract

BACKGROUND: Deep learning (DL) models for accurate renal tumor characterization may benefit from multi-center datasets for improved generalizability; however, data-sharing constraints necessitate privacy-preserving solutions like federated learning (FL).

Authors

  • Dat-Thanh Nguyen
    Tufts University School of Medicine, Boston, Massachusetts, USA.
  • Maliha Imami
    Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland.
  • Lin-Mei Zhao
    National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Hunan, 410008, People's Republic of China.
  • Jing Wu
    School of Pharmaceutical Science, Jiangnan University, Wuxi, 214122, Jiangsu, China.
  • Ali Borhani
    The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, The Johns Hopkins Hospital, 1800 Orleans Street, Baltimore, MD 21287 (A.B., G.Z., I.R.K., S.L.Z., B.A.V.).
  • Alireza Mohseni
    Johns Hopkins University School of Medicine, 600 N. Wolfe Street / Phipps 446, Baltimore, MD 21287, USA. Electronic address: amohsen4@jhmi.edu.
  • Mihir Khunte
    Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut.
  • Zhusi Zhong
    Radiology AI Lab, Brown University, Providence, Rhode Island, USA.
  • Victoria Shi
    Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Sophie Yao
    Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Yuli Wang
    School of Control Science and Engineering, Shandong University, Jinan 250061, P.R.China.
  • Nicolas Loizou
    Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, Maryland, USA.
  • Alvin C Silva
    Department of Radiology, Mayo Clinic Hospital, Scottsdale, Arizona.
  • Paul J Zhang
    Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.
  • Zishu Zhang
    Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China. S.Stavropoulos@uphs.upenn.edu Harrison_Bai@Brown.edu zishuzhang@csu.edu.cn.
  • Zhicheng Jiao
  • Ihab Kamel
    Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Maryland.
  • Wei-Hua Liao
    From the Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China (H.X.B., Z.X., D.C.W., W.H.L.); Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI (H.X.B., B.H., K.H., I.P., M.K.A.); Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pa (R.W.); Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology. Massachusetts General Hospital, Boston, Mass (K.C.); Warren Alpert Medical School at Brown University, Providence, RI (H.X.B., K.H., T.M.L.T., J.W.C., I.P.); Department of Radiology, Yongzhou Central Hospital, Yongzhou, China (L.B.S.); Department of Radiology, Changde Second People's Hospital, Changde, China (J.M.); Department of Radiology, Affiliated Nan Hua Hospital, University of South China, Hengyang, China (X.L.J.); Department of Radiology, Loudi Central Hospital, Loudi, China (Q.H.Z.); Department of Radiology, Chenzhou Second People's Hospital, Chenzhou, China (P.F.H.); Department of Radiology, Zhuzhou Central Hospital, Zhuzhou, China (Y.H.L.); Department of Radiology, Yiyang City Center Hospital, Yiyang, China (F.X.F.); Department of Radiology, Brigham and Women's Hospital, Boston, Mass (R.Y.H.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (R.S.); and Department of Radiology, The First Hospital of Changsha, Changsha, China (Q.Z.Y.).
  • Harrison Bai
    Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States.

Keywords

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