XGA-Osteo: Towards XAI-Enabled Knee Osteoarthritis Diagnosis with Adversarial Learning
XGA-Osteo: Towards XAI-Enabled Knee Osteoarthritis Diagnosis with Adversarial Learning
Hieu Phan, Loc Le, Mao Nguyen, Phat Nguyen, Sang Nguyen, Minh-Triet Tran, Tho Quan
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Demo Track. Pages 8771-8775.
https://doi.org/10.24963/ijcai.2024/1029
This research introduces XGA-Osteo, an innovative approach that leverages Explainable Artificial Intelligence (XAI) to enhance the accuracy and interpretability of knee osteoarthritis diagnosis. Recent studies have utilized AI approaches to automate the diagnosis using knee joint X-ray images. However, these studies have primarily focused on predicting the severity of osteoarthritis without providing additional information to assist doctors in their diagnoses. In addition to accurately diagnosing the severity of the condition, XGA-Osteo generates an anomaly map, produced from a reconstructed image of a healthy knee using adversarial learning. Thus, the abnormal regions in X-ray images can be highlighted, offering valuable supplementary information to medical experts during the diagnosis process.
Keywords:
Multidisciplinary Topics and Applications: MDA: Health and medicine
Machine Learning: ML: Autoencoders
Machine Learning: ML: Generative adverserial networks
Machine Learning: ML: Unsupervised learning