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纯视觉变压器 (CT-ViT) 带有Noise2Neighbors插曲,用于低剂量CT图像去除

Luella Marcos¹, Paul Babyn², Javad Alirezaie³

¹Department of Electrical, Biomedical and Computer Engineering, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, M5B 2K3, Ontario, Canada.

Journal of imaging informatics in medicine

|April 15, 2024

概括

视觉转换器 (ViT) 通过克服卷积神经网络 (CNN) 的局限性来增强医疗图像的否定. 这种纯粹的ViT模型在低剂量CT扫描中显著提高了图像质量和细节保存.

科学领域:

医疗成像医学成像
人工智能的人工智能
计算机视觉计算机视觉

背景情况:

卷积神经网络 (CNN) 面临医疗图像处理方面的挑战,包括有限的特征表示和计算成本.
变压器提供了一个有希望的替代方案,以解决医疗图像分析中的这些CNN限制.
准确的患者诊断依赖于医疗图像中保存精细的空间细节.

研究的目的:

为低剂量计算机断层扫描 (LDCT) 图像处理引入基于纯视觉变压器 (ViT) 的无噪模型.
评估ViT模型与基于CNN和混合CNN-ViT模型对LDCT宣传的有效性.
评估模型能够保存高频和低频信息以及细节结构细节的能力.

主要方法:

开发了一个U-Net框架,包含视觉变压器 (ViT) 模块和Noise2Neighbor (N2N) 插值.
拟议的纯ViT模型在五个不同的低剂量和正常剂量CT图像对数据集上进行了训练和测试.
与已建立的CNN和混合模型进行了定量 (SSIM,PSNR) 和视觉比较.

主要成果:

与纯CNN模型相比,纯ViT模型在结构相似性指数测量 (SSIM) 和峰值信号对噪声比率 (PSNR) 中显示出15-20%的增加.
变压器中的自我注意力机制在定量指标中表现优于传统的CNN.
视觉分析证实了ViT模型在CT图像中重建精细结构细节方面的卓越性能.

结论:

纯视觉转换器有效地解决了CNN在医学图像否定方面的局限性.
拟议的基于ViT的U-Net模型在LDCT图像质量和诊断细节保存方面提供了显著的改进.
这项研究强调了变压器在推进医疗图像处理技术方面的潜力.

关键词:

在 CT 中,无声化.卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.机器学习是机器学习.医疗图像处理医学图像处理视觉变压器视觉变压器

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