通过大核注意力和多尺度融合增强YOLOv11以精确检测小和多损伤的骨瘤
Sihan Chen1, Youcheng Peng1, Yingxuan Liu2
1Sydney Smart Technology College, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China.
Diagnostics (Basel, Switzerland)
|August 28, 2025
概括
一个新的深度学习模型YOLOv11-MTB显著改善了X射线图像中主要骨瘤的检测. 这种先进的AI在识别小型和多重病变方面具有很高的准确性, 有助于早期诊断.
科学领域:
- 医学成像
- 人工智能
- 癌症学
背景情况:
- 主要骨瘤如骨肉瘤和骨髓肉瘤是罕见的,需要及时诊断的侵袭性癌症.
- 虽然可以使用X射线扫描,但很难检测到小或多焦点的骨损伤.
- 目前的深度学习模型由于单个中心数据集的小而缺乏通用性.
研究的目的:
- 开发一个先进的深度学习模型以精确地检测X光片中的骨瘤.
- 提高小型和多焦点病变的检测能力,解决现有方法的局限性.
主要方法:
- 介绍YOLOv11-MTB,这是一个增强的YOLOv11模型,包含基于变压器的多尺度注意力,边界感知特征融合和受感场增强.
- 在两个多中心数据集上进行培训和评估,包括注释骨损伤数据的BTXRD数据集.
- 专注于改善检测小型和多焦点病变.
主要成果:
- 在骨瘤检测方面,YOLOv11-MTB取得了最先进的性能.
- 在BTXRD数据集中,该模型的平均精度为79.6%.
- 临床相关的结果包括小病变的mAP为55. 8%,多病变的mAP为63. 2%.
结论:
- YOLOv11-MTB框架在主要骨瘤检测方面表现出强大的概括性和准确性.
- 该模型在识别小型和多重病变方面的有效性表明其在放射学中具有重要的临床应用潜力.
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