Uncertainty-based Active Learning by Bayesian U-Net for Multi-label Cone-beam CT Segmentation.

Journal: Journal of endodontics
PMID: 37979653

Abstract

INTRODUCTION: Training of Artificial Intelligence (AI) for biomedical image analysis depends on large annotated datasets. This study assessed the efficacy of Active Learning (AL) strategies training AI models for accurate multilabel segmentation and detection of periapical lesions in cone-beam CTs (CBCTs) using a limited dataset.

Authors

  • Jiayu Huang
    School of Computing and Augmented Intelligence Arizona State University, Tempe, Arizona.
  • Nazbanoo Farpour
    Department of Endodontics, University of Pennsylvania, Philadelphia, Pennsylvania.
  • Bingjian J Yang
    Department of Endodontics, University of Pennsylvania, Philadelphia, Pennsylvania.
  • Muralidhar Mupparapu
    Department of Oral Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
  • Fleming Lure
    MS Technologies Corp, Rockville, MD USA.
  • Jing Li
    Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
  • Hao Yan
    Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas 75235.
  • Frank C Setzer
    Department of Endodontics, School of Dental Medicine, University of Pennsylvania, Philadelphia, Pennsylvania. Electronic address: fsetzer@upenn.edu.

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