Semi-supervised learning for predicting total knee replacement with unsupervised data augmentation
| Autor: | Kyunghyun Cho, Cem M. Deniz, Jimin Tan, Gregory Chang, Bofei Zhang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
musculoskeletal diseases
medicine.medical_specialty business.industry Joint replacement Degenerative Disorder medicine.medical_treatment Total knee replacement Semi-supervised learning Osteoarthritis medicine.disease Convolutional neural network Total knee Physical medicine and rehabilitation Medicine business Outcome prediction |
| Zdroj: | Medical Imaging 2020: Computer-Aided Diagnosis. |
| Popis: | 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). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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