Abstract #1890

Unsupervised Domain Adaptation for Automated Knee Osteoarthritis Phenotype Classification

Junru Zhong¹, Yongcheng Yao¹, Dόnal G. Cahill², Fan Xiao³, Siyue Li¹, Jack Lee⁴, Kevin Ki-Wai Ho⁵, Michael Tim-Yun Ong⁵, James F. Griffith², and Weitian Chen¹

¹CU Lab for AI in Radiology (CLAIR), Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Sha Tin, NT, Hong Kong, ²Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Sha Tin, NT, Hong Kong, ³Department of Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China, ⁴Centre for Clinical Research and Biostatistics, The Chinese University of Hong Kong, Sha Tin, NT, Hong Kong, ⁵Department of Orthopaedics & Traumatology, The Chinese University of Hong Kong, Sha Tin, NT, Hong Kong

Synopsis

Keywords: Osteoarthritis, Osteoarthritis

We propose a knee osteoarthritis phenotype classification system using unsupervised domain adaptation (UDA). A convolutional neural network was initially trained on a large source dataset (Osteoarthritis Initiative, n=3116), then adapted to a small target dataset (n=50). We observed a significant performance improvement compared to the classifiers trained solely on the target dataset. We demonstrated the feasibility of applying UDA for medical image analysis in a small label-free dataset.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.