Integrating model explanations and hybrid priors into deep stacked networks for the "safe zone" prediction of acetabular cup.

Journal: Acta radiologica (Stockholm, Sweden : 1987)
Published Date: Aug 22, 2022

Abstract

BACKGROUND: Existing state-of-the-art "safe zone" prediction methods are statistics-based methods, image-matching techniques, and machine learning methods. Yet, those methods bring a tension between accuracy and interpretability.

Authors

  • Fuchang Han
    School of Computer Science and Engineering, 12570Central South University, Changsha, PR China.
  • Shenghui Liao
    School of Computer Science and Engineering, 12570Central South University, Changsha, PR China.
  • Sifan Bai
    School of Computer Science and Engineering, 12570Central South University, Changsha, PR China.
  • Renzhong Wu
    School of Computer Science and Engineering, 12570Central South University, Changsha, PR China.
  • Yingqi Zhang
    Tongji Hospital, School of Medicine, 12476Tongji University, Shanghai, PR China.
  • Yongqiang Hao
    Ninth People's Hospital, 12474Shanghai Jiao Tong University School of Medicine, Shanghai, PR China.

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