交叉ViT与ECAP:增强深度学习用于下病变分类.
Wannakamon Panyarak1, Wattanapong Suttapak2, Phattaranant Mahasantipiya1
1Division of Oral and Maxillofacial Radiology, Department of Oral Biology and Diagnostic Sciences, Faculty of Dentistry, Chiang Mai University, Suthep Road, Suthep Sub-district, Mueang Chiang Mai District, Chiang Mai 50200, Thailand.
International journal of medical informatics
|November 4, 2024
概括
包括CrossViT和ResNet在内的深度学习模型显示,使用扩展切割和填充 (ECAP) 技术对放射性病变进行了改进的分类. 交叉ViT模型的表现优于ResNets,证明了准确诊断的潜力.
科学领域:
- 口腔和面放射学 口腔和面放射学
- 人工智能在医学中的应用
- 医学图像分析 医学图像分析
背景情况:
- 放射性下病变如乳腺母细胞瘤 (AM),牙状囊 (DC),牙生囊 (OKC) 和根性囊 (RC) 存在诊断挑战,原因是重叠的放射性特征.
- 深度学习,特别是多尺度视觉转换器 (CrossViT) 和卷积神经网络 (ResNets),为自动图像分类提供了潜力.
- 扩展切割和填充 (ECAP) 技术旨在增加训练数据,同时保持上下文,此前尚未应用于牙科放射学分类.
研究的目的:
- 评估CrossViT模型与ECAP技术相结合的有效性,用于对常见的放射性病变进行分类.
- 为了比较CrossViT和ResNet架构的性能,有和没有ECAP,在区分AM,DC,OKC和RC之间.
- 评估每个模型配置的诊断性能指标,包括准确性,精度,回忆,F1得分和AUC.
主要方法:
- 对208个放射性下病变 (AM,DC,OKC,RC) 的回顾性分析,这些病变来自全景放射/OPG,具有确定的组织学诊断.
- 实施横向翻转和ECAP数据增强技术.
- 使用四重交叉验证进行CrossViT-15,CrossViT-18,ResNet-50,ResNet-101和ResNet-152模型的培训和评估.
主要成果:
- 无论ECAP应用程序如何,CrossViT模型在准确性,精度,回忆和F1得分方面始终优于ResNet模型.
- 在一般情况下,ECAP技术改善了模型性能,ResNet-152.2的F1得分在统计学上显著增加.
- 对于所有病变类型,CrossViT-18显示出最佳的整体性能,而CrossViT-15则实现了所有病变类型的AUC超过0.80. 牙囊的AUC值最高,牙生囊的AUC值最低.
结论:
- 通过保留上下文信息,ECAP技术提高了放射性病变分类的深度学习模型性能.
- 交叉ViT模型,特别是当加上ECAP时,显示出对这些病变的准确和可靠分类有很大的希望.
- 这种方法对于改善罕见病变的分类特别有价值,因为培训数据可能有限.
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