用注意力模块提升儿童腕部骨折检测准确率
Pediatric Wrist Fracture Detection Using Feature Context Excitation Modules in X-ray Images
- 在YOLOv8中加入四种上下文注意力模块增强特征提取
- 最高mAP@50达67.07%，比当前最佳模型更准且更快
- 适合医学影像辅助诊断系统开发者参考
儿童日常生活中常发生腕部创伤，通常需放射科医生分析X光片后由外科医生决定手术方案。深度学习的发展使神经网络成为医疗影像诊断的计算机辅助诊断（CAD）工具。由于YOLOv8在目标检测任务中表现优异，已被用于多种骨折检测。本文提出四种FCE-YOLOv8变体，分别集成Squeeze-and-Excitation（SE）、Global Context（GC）、Gather-Excite（GE）和Gaussian Context Transformer（GCT）模块以提升模型性能。在GRAZPEDWRI-DX数据集上的实验表明，所提YOLOv8+GC-M3模型将mAP@50从65.78%提升至66.32%，优于现有最先进（SOTA）模型并减少推理时间；而YOLOv8+SE-M3模型达到最高mAP@50为67.07%，超越当前SOTA表现。代码已开源：https://github.com/RuiyangJu/FCE-YOLOv8。
原文摘要 · Abstract (English)
Children often suffer wrist trauma in daily life, while they usually need radiologists to analyze and interpret X-ray images before surgical treatment by surgeons. The development of deep learning has enabled neural networks to serve as computer-assisted diagnosis (CAD) tools to help doctors and experts in medical image diagnostics. Since YOLOv8 model has obtained the satisfactory success in object detection tasks, it has been applied to various fracture detection. This work introduces four variants of Feature Contexts Excitation-YOLOv8 (FCE-YOLOv8) model, each incorporating a different FCE module (i.e., modules of Squeeze-and-Excitation (SE), Global Context (GC), Gather-Excite (GE), and Gaussian Context Transformer (GCT)) to enhance the model performance. Experimental results on GRAZPEDWRI-DX dataset demonstrate that our proposed YOLOv8+GC-M3 model improves the mAP@50 value from 65.78% to 66.32%, outperforming the state-of-the-art (SOTA) model while reducing inference time. Furthermore, our proposed YOLOv8+SE-M3 model achieves the highest mAP@50 value of 67.07%, exceeding the SOTA performance. The implementation of this work is available at https://github.com/RuiyangJu/FCE-YOLOv8.
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