An Attention Network With Self-Supervised Learning for Rheumatoid Arthritis Scoring

Deyu Ling, Wenxin Yu, Zhiqiang Zhang, Jinmei Zou

Abstract: Rheumatoid arthritis (RA) is a chronic disease causing joint pain and disability. Early treatment can prevent irreversible bone damage. The Sharp/van der Heijde (SvH) method, a common clinical standard for assessing RA progression, is manual and subjective, leading to inconsistent measurements. To overcome this, we propose a deep learning model based on the SvH scoring criteria to predict SvH scores of RA patients’ hands. Our efficient attention network, ECBANet, merges supervised and self-supervised learning to generate attentional weights without losing feature dimensionality. It effectively captures the local features of the RA hand and enhances the model’s focus on crucial cues like joint position and gap. Extensive experiments on the only public dataset of hand X-ray images of RA patients labeled with SvH scores show that our model surpasses the current optimal model for this dataset, reducing MAE by 2% and RMSE by 7.4%.