Quadrant Segmentation VLM with Few-Shot Adaptation and OCT Learning-based Explainability Methods for Diabetic Retinopathy
Published:Dec 20, 2025 17:45
•1 min read
•ArXiv
Analysis
This article describes a research paper on using a Vision-Language Model (VLM) for diagnosing Diabetic Retinopathy. The approach involves quadrant segmentation, few-shot adaptation, and OCT-based explainability. The focus is on improving the accuracy and interpretability of AI-based diagnosis in medical imaging, specifically for a challenging disease. The use of few-shot learning suggests an attempt to reduce the need for large labeled datasets, which is a common challenge in medical AI. The inclusion of OCT data and explainability methods indicates a focus on providing clinicians with understandable and trustworthy results.
Key Takeaways
- •Applies VLM to diagnose Diabetic Retinopathy.
- •Employs quadrant segmentation, few-shot adaptation, and OCT-based explainability.
- •Aims to improve accuracy and interpretability of AI diagnosis in medical imaging.
- •Uses few-shot learning to potentially reduce the need for large datasets.
- •Includes OCT data and explainability methods for clinician understanding.
Reference
“The article focuses on improving the accuracy and interpretability of AI-based diagnosis in medical imaging.”