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

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

doi:10.1117/12.2551357

16 March 2020 Semi-supervised learning for predicting total knee replacement with unsupervised data augmentation

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

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 113140P (2020) https://doi.org/10.1117/12.2551357
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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).

Conference Presentation

Citation Download Citation

Jimin Tan, Bofei Zhang, Kyunghyun Cho, Gregory Chang, and Cem M. Deniz "Semi-supervised learning for predicting total knee replacement with unsupervised data augmentation", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113140P (16 March 2020); https://doi.org/10.1117/12.2551357

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available