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Imaging Studies II: Ultrasonography

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: Jul 12, 2025

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

乳房超声波图像增大和细分使用GAN与身份块和修改的U-Net 3使用.

Meshrif Alruily¹, Wael Said^2,3, Ayman Mohamed Mostafa¹

¹College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.

Sensors (Basel, Switzerland)

|October 28, 2023

概括

本研究引入了一种混合方法来检测乳腺癌,使用生成对抗网络 (GAN) 来进行图像增强和修改的U-Net 3+进行细分,提高早期检测的准确性.

关键词:

在U-Net 3+上使用.增强增强增强增强乳腺癌乳腺癌乳腺癌细分化细分化的细分化超声波超声波的使用情况.

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

相关实验视频

Last Updated: Jul 12, 2025

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

科学领域:

医疗成像医学成像
医疗保健中的人工智能
在瘤学瘤学.

背景情况:

乳腺癌是妇女死亡的主要原因之一.
早期检测可显著改善治疗结果和生存率.
医疗图像的准确细分和增强对于诊断工具至关重要.

研究的目的:

开发一种混合框架,通过图像增强和细分来增强乳腺癌检测.
为了提高乳腺癌超声波图像分析的效率和准确性.
提出一种结合先进生成对抗网络 (GAN) 和U-Net架构的新方法.

主要方法:

使用修改后的GAN进行图像增强,具有非线性身份块,标签光滑和新的丢失函数.
使用修改后的U-Net 3+架构进行图像细分.
对超声波增强和细分现有GAN和U-Net模型进行比较分析.

主要成果:

修改后的GAN在超声波增强中取得了卓越的性能,其初始得分为14.32和Fréchet初始距离 (FID) 为41.86.86.
经过修改的U-Net 3+显示了高的细分精度,获得了95.49%的子得分和95.67%的精度.
混合方法在增强和细分任务中表现优于其他方法.

结论:

拟议的混合框架为乳腺癌超声波图像分析提供了高效和有效的解决方案.
新的GAN和U-Net修改有助于提高早期发现乳腺癌的诊断能力.
这种方法具有显著的潜力,可以提高乳腺癌诊断中的临床决策.