现代电子技术2025，Vol.48Issue(9)：15-23,9.DOI:10.16652/j.issn.1004-373x.2025.09.003
基于多尺度焦点自注意力的膝骨关节炎分级方法
Knee osteoarthritis grading method based on multi-scale focal self-attention
摘要
Abstract
Early detection and grading of knee osteoarthritis(KOA)is an important diagnostic basis for improving the quality of life of chronic arthritis patients.Therefore,a KOA hierarchical model MSFFormer(multi-scale focal transformer)based on multi-scale focal self-attention is proposed.In the model,the dominant spatial prior technology is combined with the self-attention mechanism,the Y-axial spatial decay mechanism is designed to construct the two-dimensional dominant spatial prior matrix,and the multi-scale focus attention is proposed in combination with the self-attention mechanism to make the model pay more attention to the correlation features between the lesion area and adjacent areas in KOA images,while reducing the attention redundancy caused by irrelevant background.In comparison with the other advanced methods in the open knee osteoarthritis dataset OAI(osteoarthritis initiative),the accuracy of MSFFormer in the early KOA two-grading tasks and KOA five-class KL(Kellgren and Lawrence system)grading tasks has improved by 1.73%and 0.88%,respectively.Therefore,it is verified that the MSFFormer is more conducive to the early detection and grading of KOA.关键词
计算机辅助诊断/X线片图像/膝骨关节炎/深度学习/Transformer/自注意力机制Key words
computer aided diagnosis/X-ray image/knee osteoarthritis/deep learning/Transformer/self-attention mechanism分类
电子信息工程引用本文复制引用
蔡晓宇,汪宇玲..基于多尺度焦点自注意力的膝骨关节炎分级方法[J].现代电子技术,2025,48(9):15-23,9.基金项目
国家自然科学基金项目(62066003) （62066003）
国家留学基金项目(CSC202208360143) （CSC202208360143）
