Semi-supervised learning for predicting total knee replacement with unsupervised data augmentation

Jimin Tan, Bofei Zhang, Kyunghyun Cho, Gregory Chang, Cem M. Deniz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Osteoarthritis (OA) is a chronic degenerative disorder of joints and is the most common reason leading to total knee joint replacement (TKR). In this paper, we implemented a semi-supervised learning approach based on Unsupervised Data Augmentation (UDA) along with valid perturbations for radiographs to enhance the performance of supervised TKR outcome prediction model. Our results suggest that the use of semi-supervised approach provides superior results compared to the supervised approach (AUC of 0.79 ± 0.04 vs 0.74 ± 0.04).

Original languageEnglish (US)
Title of host publicationMedical Imaging 2020
Subtitle of host publicationComputer-Aided Diagnosis
EditorsHorst K. Hahn, Maciej A. Mazurowski
PublisherSPIE
ISBN (Electronic)9781510633957
DOIs
StatePublished - 2020
EventMedical Imaging 2020: Computer-Aided Diagnosis - Houston, United States
Duration: Feb 16 2020Feb 19 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11314
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2020: Computer-Aided Diagnosis
CountryUnited States
CityHouston
Period2/16/202/19/20

Keywords

  • convolutional neural networks
  • osteoarthritis
  • semi-supervised learning
  • total knee replacement
  • unsupervised data augmentation
  • x-ray

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Tan, J., Zhang, B., Cho, K., Chang, G., & Deniz, C. M. (2020). Semi-supervised learning for predicting total knee replacement with unsupervised data augmentation. In H. K. Hahn, & M. A. Mazurowski (Eds.), Medical Imaging 2020: Computer-Aided Diagnosis [113140P] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 11314). SPIE. https://doi.org/10.1117/12.2551357