YOLO-VML:一个改进的物体检测模型,用于 IoMT 中的芽细胞和原核定位
Aiyun Shen1,2, Chang Li1,2, Jingwei Yang3
1Department of Biomedical EngineeringHefei University of Technology Hefei 230009 China.
IEEE open journal of engineering in medicine and biology
|February 11, 2026
概括
一个新的AI模型,YOLO-VML,准确地检测胚胎中的芽细胞和前核,改善胚胎发育研究和辅助生殖技术.
科学领域:
- 胚胎学 胚胎学
- 计算机视觉 计算机视觉
- 辅助生殖技术 辅助生殖技术
背景情况:
- 对胚胎发育研究和辅助生殖技术来说,精确检测芽细胞和前核至关重要.
- 挑战包括重叠的芽细胞,小的前核大小,相似的背景和不清楚的边界,使局部化变得困难.
研究的目的:
- 根据YOLOv10框架开发一个改进的检测模型,YOLO-VML,以应对局部化胚芽细胞和前核的挑战.
主要方法:
- 将VMamba的视觉状态空间 (VSS) 模块集成到骨干中,以增强全球受感场.
- 实施多分支加权特征金字塔网络 (MBFPN) 以改善特征融合,特别是对于小目标.
- 使用轻量级的共享卷积检测头 (LSCD) 来降低计算开销,同时保持准确性.
主要成果:
- 在检测两种细胞类型方面,YOLO-VML实现了高性能.
- 在前核检测方面获得了93.2%的平均平均精度 (mAP@0.5).
- 在超越4细胞阶段的芽细胞体检中,获得了92.3%的平均平均精度 (mAP@0.5).
结论:
- 通过增强的特征表示和检测效率,YOLO-VML有效地克服了本地化困难.
- 该模型的高精度和效率为胚胎研究和辅助生殖技术应用提供了宝贵的工具.
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