Share Email Print

Proceedings Paper

Semi-supervised learning for predicting total knee replacement with unsupervised data augmentation
Author(s): Jimin Tan; Bofei Zhang; Kyunghyun Cho; Gregory Chang; Cem M. Deniz
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper 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).

Paper Details

Date Published: 16 March 2020
PDF: 9 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113140P (16 March 2020); doi: 10.1117/12.2551357
Show Author Affiliations
Jimin Tan, New York Univ. (United States)
Bofei Zhang, New York Univ. (United States)
Kyunghyun Cho, New York Univ. (United States)
Gregory Chang, NYU Langone Health (United States)
Cem M. Deniz, NYU Langone Health (United States)

Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?