Image-based Joint-level Detection for Inflammation in Rheumatoid Arthritis from Small and Imbalanced Data

TL;DR

This paper presents a novel framework for detecting rheumatoid arthritis inflammation from RGB hand images, addressing data scarcity and imbalance issues with self-supervised pretraining and imbalance-aware training, achieving improved detection performance.

Contribution

It introduces a new inflammation detection framework combining global local encoding, self-supervised pretraining, and imbalance-aware training for RGB images.

Findings

Improved F1-score by 0.2 points over baseline

Enhanced Gmean by 0.25 points

Constructed a dedicated dataset demonstrating detection difficulty

Abstract

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.