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口腔癌病变的自动分类:视觉转换器与放射学相比

Eva Chilet-Martos¹, Joan Vila-Francés¹, José V Bagan²

¹IDAL, Electronic Engineering Department, ETSE-UV, University of Valencia, Avgda. Universitat s/n, Burjassot, València, 46100, Spain.

Computers in biology and medicine

|February 28, 2025

概括

这项研究比较了视觉转换器 (ViT) 和放射学方法用于口腔癌检测. 结合ViT-放射学方法表现出强的表现,特别是有限的数据,突出了早期口腔癌诊断的潜力.

科学领域:

医疗成像医学成像
人工智能的人工智能
在瘤学瘤学.

背景情况:

早期诊断口腔癌可以显著改善患者的治疗结果和生活质量.
基于变压器的模型正在成为医学图像分析的强大工具.

研究的目的:

为了比较视觉变压器 (ViT) 分类方法的有效性与口腔损伤分类的全自动化放射学方法.
评估一个结合ViT-放射学策略,以提高口腔癌的检测.

主要方法:

使用基于变压器的算法 (DETR,SAM,ViT) 对322张口腔病变图像 (120种癌症,202种对照) 进行分析.
比较ViT与完整的图像,边界框和细分;radiomics与边界框和细分.
开发了一种结合ViT-放射学方法,使用ViT注意力图作为放射学面具.
在模型评估中采用五重交叉验证.

主要成果:

结合ViT-radiomics模型在小型训练设备上实现了高性能 (例如97%的准确性).
独立地,使用整个图像的ViT产生了最好的结果 (99%的特异性).
使用ViT注意力口罩的放射学显示出有希望的结果 (94%的准确性),手动细分提高了这两种方法的性能.

结论:

视觉变压器 (ViT) 分类优于放射学方法,组合方法显示了可比结果.
ViT注意力地图可能会在控制器中突出显示遥远的区域,这表明在特征比较任务中要谨慎.
为了比较口腔癌和对照病变之间的特征,建议采用自动细分的放射学方法.

关键词:

口腔癌是一种口腔癌.无线电学 (Radiomics) 是一种辐射学.基于变压器的物体检测.基于变压器的细分是基于变压器的.视觉变压器视觉变压器

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