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相关概念视频

Imaging Studies II: Ultrasonography01:24

Imaging Studies II: Ultrasonography

IntroductionUltrasonography, or renal ultrasound, is a noninvasive medical imaging technique that uses high-frequency sound waves to visualize the kidneys, ureters, bladder, and surrounding tissues.Indications for Urinary System UltrasonographyUrinary system ultrasonography is indicated in various clinical scenarios, such as:Kidney Stones (Urolithiasis): To detect and monitor the size and presence of kidney or urinary tract stones.Hydronephrosis: To assess the dilation of the renal pelvis and...

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相关实验视频

Updated: Jun 28, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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乳腺瘤超声图像的多实例分类使用卷积神经网络和转移学习.

Alexandru Ciobotaru1, Maria Aurora Bota2, Dan Ioan Goța1

  • 1Department of Automation, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.

Bioengineering (Basel, Switzerland)
|December 23, 2023
PubMed
概括

这项研究开发了一种定制的深度学习模型,用于乳房超声波图像分类. 定制模型实现了高精度,在检测良性和恶性乳腺质量方面超过了几种最先进的模型.

关键词:
卷积神经网络是一种卷积神经网络.乳腺癌 乳腺癌 乳腺癌计算机视觉 计算机视觉深度学习是一种深度学习.转移学习转移学习超声波图像的超声波图像可以看到.

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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:

  • 乳腺癌是全球妇女死亡的主要原因.
  • 自动早期检测和乳腺质量的分类对于改善患者的治疗结果至关重要.
  • 超声波成像对于诊断乳腺癌至关重要,但其准确性取决于专家的专业知识.

研究的目的:

  • 为了比较六种深度学习模型对乳房超声波图像分类的效率.
  • 引入和评估一个定制的深度学习模型,用于乳腺质量分类.
  • 解决乳腺癌诊断中快速可靠的自动检测算法的需求.

主要方法:

  • 转移学习被用来微调六种最先进的深度学习模型 (ResNet-50,Inception-V3,Inception-ResNet-V2,MobileNet-V2,VGG-16,DenseNet-121).这些模型中的每一个都具有不同的特点.
  • 开发了一个定制的深度学习模型,并在增强超声波图像数据集上从头开始进行训练.
  • 用公共和私人数据集的精度,回忆,F1-Score和特异性来评估模型性能.

主要成果:

  • 在7800张图像的增强数据集上训练的模型表现出卓越的性能.
  • 定制模型在私人数据集上实现了高精度 (96.75 ± 0.26%),超过了一些已建立的模型.
  • 特定的最先进的模型,如DenseNet-121和VGG-16,显示出高精度 (分别为98.11 ± 0.10%和97.77 ± 0.29%).

结论:

  • 定制开发的深度学习模型在对乳房超声波图像进行分类方面表现出了竞争力.
  • 拟议的模型实现了高精度和高效的训练时间,表明其临床应用的潜力.
  • 这项研究有助于开发可靠的自动化系统,用于早期发现乳腺癌.