Unsupervised Domain Adaptation for Automated Knee Osteoarthritis Phenotype Classification

by   Junru Zhong, et al.

Purpose: The aim of this study was to demonstrate the utility of unsupervised domain adaptation (UDA) in automated knee osteoarthritis (OA) phenotype classification using a small dataset (n=50). Materials and Methods: For this retrospective study, we collected 3,166 three-dimensional (3D) double-echo steady-state magnetic resonance (MR) images from the Osteoarthritis Initiative dataset and 50 3D turbo/fast spin-echo MR images from our institute (in 2020 and 2021) as the source and target datasets, respectively. For each patient, the degree of knee OA was initially graded according to the MRI Osteoarthritis Knee Score (MOAKS) before being converted to binary OA phenotype labels. The proposed UDA pipeline included (a) pre-processing, which involved automatic segmentation and region-of-interest cropping; (b) source classifier training, which involved pre-training phenotype classifiers on the source dataset; (c) target encoder adaptation, which involved unsupervised adaption of the source encoder to the target encoder and (d) target classifier validation, which involved statistical analysis of the target classification performance evaluated by the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity and accuracy. Additionally, a classifier was trained without UDA for comparison. Results: The target classifier trained with UDA achieved improved AUROC, sensitivity, specificity and accuracy for both knee OA phenotypes compared with the classifier trained without UDA. Conclusion: The proposed UDA approach improves the performance of automated knee OA phenotype classification for small target datasets by utilising a large, high-quality source dataset for training. The results successfully demonstrated the advantages of the UDA approach in classification on small datasets.


page 6

page 10

page 11


Mitigating Uncertainty of Classifier for Unsupervised Domain Adaptation

Understanding unsupervised domain adaptation has been an important task ...

Shuffle Augmentation of Features from Unlabeled Data for Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA), a branch of transfer learning wher...

Unsupervised Domain Adaptation for Acoustic Scene Classification Using Band-Wise Statistics Matching

The performance of machine learning algorithms is known to be negatively...

Unsupervised Domain Adaptation using Feature-Whitening and Consensus Loss

A classifier trained on a dataset seldom works on other datasets obtaine...

Improved Benthic Classification using Resolution Scaling and SymmNet Unsupervised Domain Adaptation

Autonomous Underwater Vehicles (AUVs) conduct regular visual surveys of ...

Application of Unsupervised Domain Adaptation for Structural MRI Analysis

The primary goal of this work is to study the effectiveness of an unsupe...

Unsupervised Domain Adaptation from Axial to Short-Axis Multi-Slice Cardiac MR Images by Incorporating Pretrained Task Networks

Anisotropic multi-slice Cardiac Magnetic Resonance (CMR) Images are conv...

Please sign up or login with your details

Forgot password? Click here to reset