现代信息科技2025，Vol.9Issue(6)：110-115,6.DOI:10.19850/j.cnki.2096-4706.2025.06.021
基于多模态的干眼图像描述与分级研究
Research on Dry Eye Image Description and Grading Based on Multi-mode
摘要
Abstract
s:Ocular surface fluorescein staining images hold significant clinical value in the diagnosis and assessment of dry eye.However,manual evaluation is time-consuming and labor-intensive,with scores among the different physicians affecting consistency.To enhance diagnostic efficiency and accuracy,this paper proposes OFGD-Net(Ocular Fluorescence Grading and Description Network),an automated model based on Deep Learning,which is used for grading and describing ocular surface fluorescein staining images.OFGD-Net comprises an image encoder,two decoders,and an Attention Mechanism.The encoder is responsible for extracting features,the decoders generate image description and lesion severity scores separately,and the Attention Mechanism enhances the attention to key regions.Comparative analysis with current advanced models demonstrates that the performance of OFGD-Net is superior in primary evaluation metrics,and it exhibits significant advantages in the accuracy and similarity of generated text.关键词
深度学习/多模态/干眼/眼表荧光素染色Key words
Deep Learning/multi-mode/dry eye/ocular surface fluorescein staining引用本文复制引用
张婉玉,李秀丽..基于多模态的干眼图像描述与分级研究[J].现代信息科技,2025,9(6):110-115,6.基金项目
2023年河南省高等教育教学改革研究与实践项目(研究生教育类)(2023SJGLX112Y) （研究生教育类）
2024年度河南省高等教育教学改革研究与实践项目(2024SJGLX0332) （2024SJGLX0332）
