{"type":"ScholarlyArticle","title":"XGA-Osteo: Towards XAI-Enabled Knee Osteoarthritis Diagnosis with Adversarial Learning","authors":[{"name":"Hieu Phan","slug":"hieu-phan","given":"Hieu","family":"Phan","affiliation":"Ho Chi Minh City University of Technology (HCMUT) , VNU-HCM , Ho Chi Minh City , Vietnam"},{"name":"Loc Le","slug":"loc-le","given":"Loc","family":"Le","affiliation":"Ho Chi Minh City University of Technology (HCMUT) , VNU-HCM , Ho Chi Minh City , Vietnam"},{"name":"Mao Nguyen","slug":"mao-nguyen","given":"Mao","family":"Nguyen","affiliation":"Ho Chi Minh City University of Technology (HCMUT) , VNU-HCM , Ho Chi Minh City , Vietnam"},{"name":"Phat Nguyen","slug":"phat-nguyen","given":"Phat","family":"Nguyen","affiliation":"Ho Chi Minh City University of Technology (HCMUT) , VNU-HCM , Ho Chi Minh City , Vietnam"},{"name":"Sang Nguyen","slug":"sang-nguyen","given":"Sang","family":"Nguyen","affiliation":"Ho Chi Minh City University of Technology (HCMUT) , VNU-HCM , Ho Chi Minh City , Vietnam"},{"name":"Minh-Triet Tran","slug":"minh-triet-tran","given":"Minh-Triet","family":"Tran","affiliation":"University of Science , VNU-HCM , Ho Chi Minh City , Vietnam"},{"name":"Tho Quan","slug":"tho-quan","given":"Tho","family":"Quan","affiliation":"Ho Chi Minh City University of Technology (HCMUT) , VNU-HCM , Ho Chi Minh City , Vietnam"}],"abstract":"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.","doi":"10.24963/ijcai.2024/1029","url":"https://ijcai.org/proceedings/2024/1029","pdf":"https://ijcai.org/proceedings/2024/1029.pdf","markdown":"https://ijcai.org/proceedings/2024/1029.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/1029","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Demo Track","track":"Demo Track","number":1029,"pages":{"first":8771,"last":8775},"keywords":["Machine Learning: ML: Autoencoders","Machine Learning: ML: Generative adverserial networks","Machine Learning: ML: Unsupervised learning","Multidisciplinary Topics and Applications: MDA: Health and medicine"],"videos":[],"references":[{"position":1,"raw":"[Alshareef et al., 2022] Esam Alshareef, Fawzi Omar, Yosra Lamami, Mohamed Milad, Mohamed Eswani, Sedigh Bashir, Salah Bshina, Anas Jakdoum, Asharaf Abourqee- qah, Mohamed Elbasir, and Ellafi.A. 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