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基于视觉变压器诊断的腰椎间盘,在CT成像中具有CAM分度的解释性

Qingsong Chu^1,2, Xingyu Wang^1,2, Hao Lv^1,2

¹The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China.

BMC musculoskeletal disorders

|April 29, 2025

概括

一个新的计算机断层扫描 (CT) -视觉变压器 (ViT) 框架准确诊断腰椎间盘 (LDH). 这种人工智能方法提高了医疗成像分析的诊断效率和透明度.

科学领域:

医疗成像中的人工智能
深度学习用于诊断应用程序.
放射学和医学诊断医学诊断

背景情况:

腰椎间盘 (LDH) 诊断通常依赖于解释复杂的医学图像.
计算机断层扫描 (CT) 提供了对解剖评估至关重要的多方向视图.
视觉转换器 (ViTs) 提供了分析图像数据和空间关系的高级功能.

研究的目的:

首次引入一种新的CT-ViT框架来诊断LDH.
利用CT成像和ViT架构的优势,提高诊断准确度.
将ViT模型的性能与已建立的卷积神经网络 (CNN) 进行比较.

主要方法:

一个ViT模型被开发和训练在983名患者的2100张CT图像的数据集上.
模型的性能被评估为脊椎定位和盘异常分类任务.
进行了对CNN模型进行比较分析:ResNet18,ResNet50,LeNet,AlexNet和VGG16.

主要成果:

CT-ViT模型实现了高精度,在脊椎定位方面达到97.13%,在盘异常分类方面达到93.63%.
与所有评估的CNN模型相比,ViT模型表现出卓越的性能.
使用梯度加权类激活映射 (Grad-CAM) 验证模型预测并提供可解释性.

结论:

该研究证实了ViT模型在使用CT成像诊断LDH方面的显著潜力.
这种由人工智能驱动的方法对提高临床环境中的诊断效率和透明度充满希望.
这些发现表明ViTs在医疗AI中的未来应用,以改善患者护理.

关键词:

这就是为什么CTCTCTCTCTCT 深度学习是一种深度学习.诊断的准确性诊断的准确性这是Grad-CAM.在 LDH 和 LDH 之间.医学成像医学成像这里是ViT ViT ViT

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