CA-OCL and CHAN: A novel diagnostic framework for rheumatoid arthritis integrating contradiction-aware orthogonal contrastive learning with confidence-guided hierarchical attention.

Abstract

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disorder characterized by progressive destruction of synovial joints, for which precise early diagnosis is critical to effective clinical management. Although current computer-aided diagnosis (CAD) systems show promising potential, their practical deployment remains challenged by heterogeneity in multimodal data and the inherent complexity of pathophysiological manifestations. A key limitation of existing approaches is the absence of dedicated mechanisms to reconcile inter-modal conflicts and to adequately leverage the diagnostic information present in contradictory samples, such as discordances between laboratory findings and clinical descriptions, which often leads to suboptimal diagnostic performance. To overcome these challenges, this study introduces a novel auxiliary diagnostic framework for RA based on Contradiction-Aware Orthogonal Contrastive Learning (CA-OCL) and a Confidence-guided Hierarchical Attention Network (CHAN). The proposed architecture incorporates three major innovations. First, in the data processing stage, a lightweight feature-crossing network is employed to derive robust representations from structured data, while a multi-task adapted extension of the BERT model is utilized to extract rich semantic features from unstructured textual inputs. Second, the CA-OCL module is designed to explicitly identify and learn from contradictory negative samples-a capability largely absent in conventional contrastive learning frameworks. Additionally, orthogonal constraints are applied to minimize feature redundancy across modalities, thereby preserving discriminative modality-specific information that is often obscured by methods promoting excessive feature alignment. Finally, the CHAN module dynamically modulates inter-modal contributions using confidence estimates, mitigating the risk of unilateral dominance by any single modality, a common drawback in attention-based fusion mechanisms and facilitating refined integration of conflicting information. Comprehensive experimental evaluations demonstrate that the proposed framework achieves superior performance compared to state-of-the-art methods. These results not only validate the efficacy of our approach in handling multimodal conflicts and exploiting contradictory evidence, but also highlight its significant clinical utility through effective multimodal data integration. This work addresses critical limitations in conventional CAD systems and provides an advanced paradigm for intelligent diagnostic assessment of RA.