Rheumatoid arthritis (RA) is an autoimmune disease characterized by systemic joint inflammation. Early diagnosis and tight follow-up are essential to the management of RA, as ongoing inflammation can cause irreversible joint damage. The detection of arthritis is important for diagnosis and assessment of disease activity; however, it often takes a long time for patients to receive appropriate specialist care. Therefore, there is a strong need to develop systems that can detect joint inflammation easily using RGB images captured at home. Consequently, we tackle the task of RA inflammation detection from RGB hand images. This task is highly challenging due to general issues in medical imaging, such as the scarcity of positive samples, data imbalance, and the inherent difficulty of the task itself. However, to the best of our knowledge, no existing work has explicitly addressed these challenges in RGB-based RA inflammation detection. This paper quantitatively demonstrates the difficulty of visually detecting inflammation by constructing a dedicated dataset, and we propose a inflammation detection framework with global local encoder that combines self-supervised pretraining on large-scale healthy hand images with imbalance-aware training to detect RA-related joint inflammation from RGB hand images. Our experiments demonstrated that the proposed approach improves F1-score by 0.2 points and Gmean by 0.25 points compared with the baseline model.

翻译：类风湿关节炎（RA）是一种以全身性关节炎症为特征的自身免疫性疾病。由于持续炎症可能导致不可逆的关节损伤，早期诊断与密切随访对RA管理至关重要。关节炎检测对疾病活动度的诊断与评估具有重要意义，但患者通常需要较长时间才能获得专科诊疗。因此，亟需开发能够利用居家拍摄的RGB图像便捷检测关节炎症的系统。基于此，我们致力于从RGB手部图像中检测RA炎症。由于医学影像普遍存在的阳性样本稀缺、数据不平衡及任务本身固有难度等问题，该任务极具挑战性。然而据我们所知，现有研究尚未在基于RGB图像的RA炎症检测中明确应对这些挑战。本文通过构建专用数据集定量论证了视觉检测炎症的难度，并提出一种融合全局-局部编码器的炎症检测框架。该框架结合大规模健康手部图像的自我监督预训练与不平衡感知训练，从RGB手部图像中检测RA相关关节炎症。实验表明，相较于基线模型，所提方法将F1分数提升0.2分，G均值提升0.25分。