使用视觉变压器架构在全景放射图中评估顶顶关闭 ViT-based顶顶关闭分类
Sümeyye Coşgun Baybars1, Merve Daldal1, Merve Parlak Baydoğan2
1Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Fırat University, Elazığ 23000, Turkey.
Diagnostics (Basel, Switzerland)
|September 27, 2025
概括
与卷积神经网络 (CNN) 相比,视觉变压器 (ViT) 模型在全景射线图上对开放顶点的分类方面表现出卓越的性能. ViT模型为牙科放射学决策支持系统提供了更强大,更准确的诊断潜力.
科学领域:
- 牙科 牙科是指牙科的专业.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 开放的顶部是一个影响牙成熟的发育异常.
- 在全景放射图 (OPG) 上准确分类开放顶部对于治疗规划至关重要.
- 深度学习模型为牙科放射图的自动化分析提供了潜力.
研究的目的:
- 评估基于视觉转换器 (ViT) 的深度学习模型,用于OPGs的开放顶级分类.
- 将ViT模型的诊断准确性与传统卷积神经网络 (CNN) 架构进行比较.
- 在与ViT和CNN模型一起使用时,评估各种分类器的性能.
主要方法:
- 对OPG的回顾性收集和基于观察者的标签,以获得顶级关闭状态.
- 对两个ViT模型 (基础补丁16,补丁32) 和三个CNN模型 (ResNet50,VGG19,EfficientNetB0) 的评估.
- 八个分类器 (SVM,RF,XGBoost,LR,KNN,NB,DT,MLP) 的应用与性能指标计算 (准确度,精度,回忆,F1,AUC).
主要成果:
- 带有MLP的ViT基础补丁16 384获得了最高的精度 (0.8462 ± 0.0330) 和AUC (0.914 ± 0.032).
- 与CNN相比,ViT模型表现出更平衡和更强大的性能.
- EfficientNetB0 + MLP表现出了竞争力的表现,但被最好的ViT模型所超越.
结论:
- 对于OPG的开放顶部分类,ViT模型比CNN表现优越.
- ViT模型有望被整合到牙科放射学决策支持系统中.
- 未来的研究应该探索多中心和多模式数据,以提高概括性.
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