VL-OrdinalFormer: Vision Language Guided Ordinal Transformers for Interpretable Knee Osteoarthritis Grading

2025-12-31

arXiv.org

AI Summary

This study addresses the challenge of distinguishing early-stage knee osteoarthritis (KOA), specifically Kellgren–Lawrence grades 1 and 2 (KL1/KL2), on X-ray images—a task hindered by low inter-rater agreement among radiologists. To this end, the authors propose VL-OrdinalFormer, a novel framework that, for the first time, integrates visual–language alignment into ordinal classification. The model combines a ViT-L/16 backbone with CORAL-based ordinal regression and a CLIP-driven semantic alignment module, leveraging clinical text prompts to guide attention toward diagnostically relevant pathological regions. Evaluated on the OAI kneeKL224 dataset, VL-OrdinalFormer achieves state-of-the-art performance, surpassing both CNN and ViT baselines in macro F1 score and overall accuracy. Its clinical interpretability and relevance are further corroborated through Grad-CAM visualizations and CLIP similarity maps.

Technology Category

• Computer Vision: Language and Vision
• Knowledge Representation and Reasoning: Knowledge Representation Languages
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Knee osteoarthritis (KOA) is a leading cause of disability worldwide, and accurate severity assessment using the Kellgren Lawrence (KL) grading system is critical for clinical decision making. However, radiographic distinctions between early disease stages, particularly KL1 and KL2, are subtle and frequently lead to inter-observer variability among radiologists. To address these challenges, we propose VLOrdinalFormer, a vision language guided ordinal learning framework for fully automated KOA grading from knee radiographs. The proposed method combines a ViT L16 backbone with CORAL based ordinal regression and a Contrastive Language Image Pretraining (CLIP) driven semantic alignment module, allowing the model to incorporate clinically meaningful textual concepts related to joint space narrowing, osteophyte formation, and subchondral sclerosis. To improve robustness and mitigate overfitting, we employ stratified five fold cross validation, class aware re weighting to emphasize challenging intermediate grades, and test time augmentation with global threshold optimization. Experiments conducted on the publicly available OAI kneeKL224 dataset demonstrate that VLOrdinalFormer achieves state of the art performance, outperforming CNN and ViT baselines in terms of macro F1 score and overall accuracy. Notably, the proposed framework yields substantial performance gains for KL1 and KL2 without compromising classification accuracy for mild or severe cases. In addition, interpretability analyses using Grad CAM and CLIP similarity maps confirm that the model consistently attends to clinically relevant anatomical regions. These results highlight the potential of vision language aligned ordinal transformers as reliable and interpretable tools for KOA grading and disease progression assessment in routine radiological practice.

Problem

• knee osteoarthritis
• KL grading
• inter-observer variability
• early-stage differentiation
• radiographic assessment

Innovation

• vision-language alignment
• ordinal regression
• interpretable AI
• knee osteoarthritis grading
• CLIP-guided transformer