FracDetNet通过双焦点注意力与多尺度校准，提升医学X光片骨折检测精度。
FracDetNet: Advanced Fracture Detection via Dual-Focus Attention and Multi-scale Calibration in Medical X-ray Imaging
- 引入双焦点注意力捕捉局部细节与全局上下文特征
- 在GRAZPEDWRI-DX数据集上达到40.0% mAP50-95，较基线提升7.5%
- 适合临床辅助诊断、影像算法研发人员使用
本文提出一种先进的骨折检测框架FracDetNet，以应对医学影像中骨折检测的挑战。尽管近期取得进展，现有方法仍难以准确识别细微且形态多样的骨折，主要受限于成像角度差异和图像质量不佳。为此，FracDetNet融合双焦点注意力（DFA）与多尺度校准（MC）机制。DFA模块通过联合全局与局部注意力机制，有效提取细节特征与整体上下文信息；MC模块则自适应优化特征表示，提升检测性能。在公开的GRAZPEDWRI-DX数据集上的实验表明，FracDetNet达到40.0%的mAP$_{50-95}$，较基线模型提升7.5%；mAP$_{50}$达63.9%，提升4.2%；特定骨折检测准确率亦提高2.9%。
原文摘要 · Abstract (English)
In this paper, an advanced fracture detection framework, FracDetNet, is proposed to address challenges in medical imaging, as accurate fracture detection is essential for enhancing diagnostic efficiency in clinical practice. Despite recent advancements, existing methods still struggle with detecting subtle and morphologically diverse fractures due to variable imaging angles and suboptimal image quality. To overcome these limitations, FracDetNet integrates Dual-Focus Attention (DFA) and Multi-scale Calibration (MC). Specifically, the DFA module effectively captures detailed local features and comprehensive global context through combined global and local attention mechanisms. Additionally, the MC adaptively refines feature representations to enhance detection performance. Experimental evaluations on the publicly available GRAZPEDWRI-DX dataset demonstrate state-of-the-art performance, with FracDetNet achieving a mAP$_{50-95}$ of 40.0\%, reflecting a \textbf{7.5\%} improvement over the baseline model. Furthermore, the mAP$_{50}$ reaches 63.9\%, representing an increase of \textbf{4.2\%}, with fracture-specific detection accuracy also enhanced by \textbf{2.9\%}.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。