Cross-Modal Semantic-Enhanced Diffusion Framework for Diabetic Retinopathy Grading
Automated grading of diabetic retinopathy (DR) faces several critical challenges: subtle inter-grade visual distinctions in fine-grained lesion patterns, distributional discrepancies induced by heterogeneous imaging devices and acquisition conditions, and the inherent inability of purely visual approaches to exploit clinical semantic knowledge. In this paper, we propose CLIP-Guided Semantic Diffusion (CGSD), a DR grading framework that synergistically integrates vision-language pretraining with diffusion probabilistic modeling. We adopt a domain-specific vision-language model tailored for DR grading as the semantic guidance module and adapt it to the target domain via Low-Rank Adaptation ...
| Authors | Yiqun Wang |
|---|---|
| Primary category | eess.IV |
| Published | 10 May 2026 |
| Last updated at source | 10 May 2026 |
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