在牙科图像细分中评估CNN,变压器和混合架构
Lisa Schneider1, Aleksander Krasowski2, Vinay Pitchika2
1Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin, Berlin, Germany.
Journal of dentistry
|March 10, 2025
概括
卷积神经网络 (CNN) 在牙图像细分任务中显著优于基于变压器的模型,包括牙,牙结构和病变. 这强调了CNN作为当前牙科AI应用的优越选择.
科学领域:
- 人工智能在牙科中的应用
- 医学图像分析 医学图像分析
- 深度学习架构 深度学习架构
背景情况:
- 卷积神经网络 (CNN) 是牙科图像分析的标准.
- 在自然语言处理中取得成功的变压器架构显示出牙成像的潜力.
- 将这些架构进行比较对于推进牙科AI至关重要.
研究的目的:
- 为了比较CNN与基于变压器和混合模型在牙图像细分中的性能.
- 为了评估不同牙科图像模式和细分任务的模型有效性.
主要方法:
- 评估了两个CNN (U-Net,DeepLabV3+),两个混合型 (SwinUNETR,UNETR) 和两个变压器 (TransDeepLab,SwinUnet).
- 使用了三个数据集:全景射线图 (牙细分) 和咬翼射线图 (牙结构和损伤细分).
- 采用5倍交叉验证来进行可靠的模型评估.
主要成果:
- 在所有三个细分任务中,CNN都表现出了卓越的性能.
- 对于牙细分,CNN实现了0.89±0.009.9的平均F1分数.
- 与变压器和混合动力相比,CNN在牙结构 (0.85±0.008) 和病损伤细分 (0.49±0.031) 上也表现出色.
结论:
- 在牙科图像细分方面,CNN显著优于基于变压器和混合架构的架构.
- 研究结果表明,CNN目前是分析牙科全景和咬翼放射图的最佳选择.
- 牙科从业者和研究人员应考虑特定的牙科成像应用的模型架构优势.
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