生物医学工程研究2025，Vol.44Issue(1)：58-66,9.DOI:10.19529/j.cnki.1672-6278.2025.01.09
融合级联Transformer和YOLOv8的膝关节多类别囊肿检测方法研究
Research on knee multi-class cyst detection algorithm based on cascaded Transformer and YOLOv8
摘要
Abstract
Aiming at the high similarity and blurred boundary between the cysts and intra-articular fluid and other tissues in mag-netic resonance(MR)images of knee cysts,we proposed a knee cysts lesion detection model YOLO-Cyst.Firstly,in the backbone network,a cascaded Vision Transformer module was employed to capture long-distance contextual information,thereby enhancing cyst detection accuracy.Secondly,building upon the cross-stage partial connectivity and dual fusion module of YOLOv8,a deformable large kernel attention module was introduced to enhance the model capacity for local feature extraction.Experimental results demonstrated that compared to the YOLOv8,YOLO-Cyst improved mAP50 and mAP50-95 by 5.1%,0.8%,respectively.Furthermore,compared to the Faster R-CNN and DETR,YOLO-Cyst enhanced mAP50 by 23.9%,13.0%,and mAP50-95 by 10.7%,6.8%,respectively.This algorithm can learn rich feature representation of knee cysts and enable accurate detection of cysts of different types and morphologies.关键词
膝关节囊肿/目标检测/上下文信息/Transformer/YOLOv8Key words
Knee cysts/Object detection/Contextual information/Transformer/YOLOv8分类
医药卫生引用本文复制引用
张丽媛,张驰,蒋振刚,唐雄风..融合级联Transformer和YOLOv8的膝关节多类别囊肿检测方法研究[J].生物医学工程研究,2025,44(1):58-66,9.基金项目
国家自然科学基金项目(U21A20390) （U21A20390）
吉林省教育厅项目(JJKH20240945KJ). （JJKH20240945KJ）
