---
title: "TRIKOP: Exploring Visual Prompting Paradigms for Multi-Grade Knee Osteoarthritis Classification on MRI Images"
authors:
  - "Hieu Phan"
  - "Hung Pham"
  - "Dat Nguyen"
  - "Khoa Le"
  - "Tuan Nguyen"
  - "Triet Tran"
  - "Tho Quan"
doi: "10.24963/ijcai.2025/1270"
url: "https://ijcai.org/proceedings/2025/1270"
pdf: "https://ijcai.org/proceedings/2025/1270.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "11095-11099"
track: "Demo Track"
keywords:
  - "Computer Vision: CV: Applications and Systems"
  - "Computer Vision: CV: Interpretability and transparency"
  - "Computer Vision: CV: 3D computer vision"
  - "Computer Vision: CV: Representation learning"
---

# TRIKOP: Exploring Visual Prompting Paradigms for Multi-Grade Knee Osteoarthritis Classification on MRI Images

Hieu Phan, Hung Pham, Dat Nguyen, Khoa Le, Tuan Nguyen, Triet Tran, Tho Quan

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 11095-11099. DOI: [10.24963/ijcai.2025/1270](https://doi.org/10.24963/ijcai.2025/1270)

## Abstract

Knee osteoarthritis (KOA) is a degenerative joint disease that significantly impacts quality of life. While transfer learning shows promise in medical imaging, its application to KOA diagnosis remains challenging due to medical data's unique characteristics. To address this, we propose TRIKOP, a framework leveraging Visual Prompting for KOA diagnosis on MRI. Our approach explores three prompt-generating strategies that extract clinically relevant information from input images. Each prompt type is encoded using a tailored method to integrate effectively into the Vision Transformer for optimal representation. Among them, the contrastive embedding prompting strategy achieves 63.04% accuracy on the OAI dataset, surpassing prior studies. Moreover, TRIKOP produces attention maps highlighting diagnostically significant regions, improving model interpretability. This work highlights TRIKOP’s potential to improve AI-driven KOA diagnosis and clinical support.