具有自我监督的多头YOLOv12预训练,用于检测尿液沉积物颗粒
Mehdi Alizadeh1, Ali Karimi2, Mohammad Javad Barikbin2
1Department of Clinical Biochemistry, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Scientific reports
|November 21, 2025
概括
这项研究引入了用于尿液沉积物分析的新深度学习模型,提高了精度和效率. 人工智能系统有效地识别各种粒子,有助于诊断脏和尿路疾病.
科学领域:
- 医学诊断 医学诊断 医学诊断
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 手动尿沉积物分析至关重要,但受到主观性和时间限制的影响.
- 现有的自动化方法缺乏用于全面粒子分类的细粒度.
- 开发一个客观和高效的自动化系统对于临床实践至关重要.
研究的目的:
- 开发一种用于自动化和可靠的尿液沉积物分析的新型深度学习方法.
- 为了使不同尿液沉积物颗粒的同时细粒度分类.
- 克服手动分析和先前的自动化技术的局限性.
主要方法:
- 使用了多头YOLOv12架构与自我监督的预训.
- 实现了切片辅助超推理 (SAHI),用于先进的对象检测.
- 创建了一个大规模的数据集 (OpenUrine),包含39个类别的790个标记和5640个未标记的图像.
主要成果:
- 在39个类数据集上实现了76.59%的精度和64.15%的平均精度 (mAP).
- 已证明具有竞争力的检测准确性,特别是对于小型和低对比度的物体.
- 六个专门的探测头使得可以进行全面的粒子分类.
结论:
- 拟议的深度学习方法在自动化尿液沉积物分析方面取得了重大进展.
- 这种人工智能驱动的方法提高了对脏和尿路疾病的诊断能力.
- 该模型的性能表明其具有临床应用和改善患者治疗结果的潜力.
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