改进YOLO模型，提升骨折检测精度与多尺度识别能力。
Enhanced Fracture Diagnosis Based on Critical Regional and Scale Aware in YOLO
- 引入关键区域选择注意力与多尺度感知头，聚焦骨折特征。
- mAP50提升4，mAP50-95提升3，达到当前最优性能。
- 适合医学影像诊断场景，尤其对小尺度骨折敏感。
骨折检测在医学影像分析中至关重要。传统诊断依赖经验丰富的医生进行视觉评估，但速度和准确性受限于专业水平。随着人工智能快速发展，基于YOLO框架的深度学习模型已被广泛用于骨折检测，显著提升了诊断效率与准确率。本文提出一种改进的YOLO模型——Fracture-YOLO，融合新型关键区域选择注意力（CRSelector）与多尺度感知（ScA）模块，进一步提升检测性能。其中，CRSelector模块利用全局纹理信息聚焦骨折关键区域；ScA模块动态调整不同尺度特征权重，增强模型在多尺度下识别骨折目标的能力。实验结果表明，相比基线模型，Fracture-YOLO在检测精度上显著提升，mAP50与mAP50-95分别提高4和3，超越基线并达到当前最优（SOTA）水平。
原文摘要 · Abstract (English)
Fracture detection plays a critical role in medical imaging analysis, traditional fracture diagnosis relies on visual assessment by experienced physicians, however the speed and accuracy of this approach are constrained by the expertise. With the rapid advancements in artificial intelligence, deep learning models based on the YOLO framework have been widely employed for fracture detection, demonstrating significant potential in improving diagnostic efficiency and accuracy. This study proposes an improved YOLO-based model, termed Fracture-YOLO, which integrates novel Critical-Region-Selector Attention (CRSelector) and Scale-Aware (ScA) heads to further enhance detection performance. Specifically, the CRSelector module utilizes global texture information to focus on critical features of fracture regions. Meanwhile, the ScA module dynamically adjusts the weights of features at different scales, enhancing the model's capacity to identify fracture targets at multiple scales. Experimental results demonstrate that, compared to the baseline model, Fracture-YOLO achieves a significant improvement in detection precision, with mAP50 and mAP50-95 increasing by 4 and 3, surpassing the baseline model and achieving state-of-the-art (SOTA) performance.
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