视力变压器为病理的分类带来了新的活力
Ji Zhang1, Jia Dan Lu2, Bo Chen1
1Department of Nephrology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, PR China.
BMC nephrology
|October 9, 2024
概括
视觉转换器 (ViT) 准确地从组织病理图像中对病理进行分类,优于传统的CNN模型. 这种人工智能方法有助于识别关键的病理特征,以改善病诊断.
科学领域:
- 人工智能在医学中的应用
- 数字病理学数字病理学
- 脏组织病理学分析 分析
背景情况:
- 人工智能 (AI) 在医学成像方面表现有前途,但由于细微的变化,对病理的分类仍然很困难.
- 视觉转换器 (ViT) 在图像识别,捕捉全球特征和增强可解释性方面提供了先进的功能.
研究的目的:
- 开发和评估视觉转换器 (ViT) 模型,以使用多种病理图像对病理进行分类.
- 为了比较ViT模型与传统卷积神经网络 (CNN) 模型在病理分类中的性能.
主要方法:
- 使用了来自635名患者的1861个整片图像 (WSI),用HE,MASSON,PAS和PASM染色.
- 提取,,并将组织图像分类为14个病理类别.
- 训练了一个经典的ViT模型 (384x384像素,16x16补丁) 并将其性能与CNN模型进行了比较.
主要成果:
- ViT模型在分类病理方面实现了更高的识别准确度 (0.96-0.99).
- 在这个分类任务中,ViT模型的表现优于传统的CNN模型.
- 视觉化显示了ViT模型能够专注于显著的病理超结构的能力.
结论:
- 与CNN相比,ViT模型在准确分类病理方面表现优越.
- ViT模型的可解释性有助于识别病诊断和治疗的关键病理特征.
- 这种由人工智能驱动的方法有可能在脏组织病理学中发现新的病理特征.
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