视觉语言模型的比较评估,用于从口腔内图像检测和定位牙病变
Maria Jahan1, Al Ibne Siam1, Lamim Zakir Pronay2
1Department of Electrical and Computer Science, North South University, Dhaka 1229, Bangladesh.
Journal of imaging
|January 27, 2026
概括
与视觉语言模型相比,YOLOv8在从口腔内图像中检测牙损伤方面表现出卓越的性能. 需要对更大的数据集进行进一步的研究,以提高佛罗伦萨-2和帕利-杰玛对牙病变分类的能力.
科学领域:
- 人工智能在牙科中的应用
- 医学成像分析 医学成像分析
- 计算机视觉 计算机视觉
背景情况:
- 内影像对诊断牙损伤至关重要.
- 视觉语言模型为牙图像的自动分析提供了潜力.
- 目前的模型需要对其在检测各种牙病变方面的效率进行评估.
研究的目的:
- 评估佛罗伦萨-2,PaLI-Gemma和YOLOv8在检测和分类牙和非牙病变方面的表现.
- 为了比较这些模型的有效性,使用标准化的口内照片数据集.
主要方法:
- 使用了172张注释式口腔内图像的数据集,对病变进行了分类,如微洞穴,洞穴病变,染色,结石和非病变等.
- 模型被训练在一个80:10:10的数据分割.
- 使用平均平均精度 (mAP),mAP50-95,以及特定类别的精度和回忆指标来评估性能.
主要成果:
- YOLOv8实现了最高的整体性能,平均平均精度 (mAP) 为37%,精度为42.3%,回忆率为31.3%,包括100%的洞穴检测精度.
- PaLI-Gemma实现了13%和21%的召回 (准确度未指明).
- 佛罗伦萨-2的平均精度为10%,精度为51%,回忆率为35%.
结论:
- 在从口腔内图像检测和分类牙病变方面,YOLOv8显著优于佛罗伦萨-2和PaLI-Gemma.
- 尽管具有多式联运能力,但佛罗伦萨-2号和帕利-杰玛号表现不佳,这表明需要更大,更多样化的数据集.
- 混合架构可能是必要的,以提高视觉语言模型在牙科成像应用中的性能.
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