在一个负担得起的嵌入式系统上使用YOLO模型进行乳房图分析.
Anongnat Intasam1, Nicholas Piyawattanametha2, Yuttachon Promworn1
1Department of Biomedical Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Ladkrabang, Bangkok 10520, Thailand.
Cancers
|January 10, 2026
概括
在NVIDIA Jetson Nano上的YOLOv11n模型为资源有限的设置提供了精确的乳房扫描病变检测. 这种负担得起的系统提供了与昂贵的GPU设置相当的性能,提高了早期乳腺癌查的可访问性.
科学领域:
- 医疗成像医学成像
- 医疗保健中的人工智能
- 计算机辅助检测 (CAD) 是指计算机辅助检测.
背景情况:
- 乳腺癌是全球女性死亡的主要原因之一.
- 乳房扫描对于早期检测至关重要,但由于缺乏熟练的放射科医生和先进的工具,在资源有限的地区面临着可访问性挑战.
- 基于深度学习的CAD系统可以自动检测和分类病变,帮助放射科医生.
研究的目的:
- 为了研究各种You Only Look Once (YOLO) 模型和用于检测乳房病变的混合卷积变压器架构 (RT-DETR) 的性能.
- 为了在一个负担得起的嵌入式系统上评估这些模型,特别是NVIDIA Jetson Nano.
- 评估在低资源的临床环境中部署先进的CAD系统的可行性.
主要方法:
- 开发了一种定制的基于Web的注释工具,以确保高乳腺造影标签准确度.
- 采用了包括来自泰国的3169名患者的数据集,并由三位专家放射科医生提供注释.
- 病变被分为六种类型:良性质 (MB),性质 (CB),相关特征良性 (AFB),恶性质 (MM),恶性性质 (CM) 和相关特征恶性 (AFM).
主要成果:
- 在NVIDIA Jetson Nano上,YOLOv11n模型表现出最佳的性能,达到0.86.6的精度.
- 在Jetson Nano上,YOLOv11n模型实现了每秒6.16±0.31的推理速度.
- 对比分析显示,Jetson Nano系统的检测性能与使用图形处理器 (GPU) 驱动的系统相提并论,但成本要低得多.
结论:
- 这项研究是第一个将高级YOLO版本集成到嵌入式乳房镜部署中的研究之一.
- 这些发现表明,在资源较低的环境中,使用负担得起的嵌入式系统用于CAD的可行性.
- 这种方法有可能显著改善乳腺查的可访问性和服务不足地区的临床应用.
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