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相关概念视频

Ultrasonography01:17

Ultrasonography

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Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called...
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Imaging Studies II: Ultrasonography01:24

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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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Ultrasound I: Abdominal Ultrasonography01:20

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Introduction:
Abdominal ultrasonography, commonly known as abdominal ultrasound, is a vital, non-invasive medical imaging technique widely used in healthcare.
Procedure:
This diagnostic tool allows the clinician to visually inspect internal structures within the abdomen, including vital organs such as the liver, gallbladder, pancreas, kidneys, and spleen.
The abdominal ultrasound process begins with applying a special gel to the patient's skin over the abdomen. This gel enhances the...
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相关实验视频

Updated: Jul 14, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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GSDA:基于生成对抗网络的半监督数据增强,用于超声波图像分类.

Zhaoshan Liu1, Qiujie Lv1,2, Chau Hung Lee3

  • 1Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore, 117575, Singapore.

Heliyon
|October 9, 2023
PubMed
概括
此摘要是机器生成的。

这项研究介绍了GSDA,一种使用生成对抗网络 (GANs) 创建高质量的医疗超声波图像的新方法. 这种数据增强技术显著提高了深度学习模型的准确性,尽管数据有限.

关键词:
卷积神经网络是一种卷积神经网络.生成性的对抗性网络.医疗图像分析 医学图像分析半监督学习 半监督学习

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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
  • 计算机视觉 计算机视觉

背景情况:

  • 医学超声波 (美国) 是一个重要的成像工具,但可变的图像质量带来了挑战.
  • 深度学习 (DL) 模型提供先进的分析,但需要广泛的数据集,这在医疗领域往往很少.
  • 现有的DL模型在有限的数据上扎,这阻碍了它们在美国医学图像分析中的表现.

研究的目的:

  • 开发一种半监督数据增强方法,GSDA,以解决美国医疗领域的数据短缺问题.
  • 利用生成对抗网络 (GAN) 合成高分辨率,高质量的美国图像.
  • 改进卷积神经网络 (CNN) 的性能,用于使用增强数据进行医学美国图像分析.

主要方法:

  • 开发了基于GAN的半监督数据增强方法GSDA,它结合了GAN和CNN.
  • 员工转移学习技术,以克服训练挑战,对GAN和CNN的数据有限.
  • 引入了一种新的评估标准,平衡分类准确性和计算时间.

主要成果:

  • 美国GSDA成功合成了高分辨率,高质量的伪标签的美国图像.
  • 该方法在仅使用780张图像的BUSI数据集上实现了97.9%的准确性.
  • 与现有的最先进的方法相比,GSDA表现出优越的性能.

结论:

  • 通过数据增强,GSDA有效地解决了美国医学成像中的数据短缺挑战.
  • 合成的高质量图像显著提高了DL模型的性能.
  • GSDA显示出强大的潜力,作为改善美国医疗分析的辅助工具.