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Imaging Studies I: Kidney, Ureter, and Bladder Studies01:28

Imaging Studies I: Kidney, Ureter, and Bladder Studies

Kidney, Ureter, and Bladder (KUB) StudiesKidney, Ureter, and Bladder (KUB) studies are standard diagnostic imaging procedures used to assess the anatomy of the urinary system. They are commonly utilized for patients experiencing abdominal pain or urinary symptoms. By using a simple X-ray of the abdomen, KUB studies can reveal structural and pathological abnormalities within the kidneys, ureters, and bladder. These studies are particularly valuable in diagnosing kidney stones, urinary...
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...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...

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Updated: May 11, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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用自主监督学习的CT图像对瘤进行分类.

Erdal Özbay1, Feyza Altunbey Özbay2, Farhad Soleimanian Gharehchopogh3

  • 1Department of Computer Engineering, Firat University, 23119, Elazig, Turkey.

Computers in biology and medicine
|May 14, 2024
PubMed
概括

一个新的深度学习模型,自我监督的学习与自我蒸用于脏瘤检测 (SSLSD-KTD),在分类脏瘤方面取得了高准确性,帮助放射科医生进行诊断.

关键词:
图像 图像 图像 图像 图像分类 分类 分类 分类.脏瘤是什么 脏瘤是什么蒙面的自动编码器自主监督学习学习

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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
  • 在瘤学瘤学.

背景情况:

  • 脏瘤是一个重要的全球健康问题,需要准确和高效的诊断方法.
  • 传统的手动瘤检测是耗时的,劳动密集的,昂贵的.
  • 深度学习 (DL) 为自动和准确的瘤检测 (KTD) 提供了一个有希望的途径.

研究的目的:

  • 开发一种更有效的DL模型,以协助医生诊断瘤.
  • 为了减少放射科医生的工作量,并尽量减少诊断错误.
  • 为了提高瘤分类的准确性和效率.

主要方法:

  • 提出了用于脏瘤检测的蒙面自编码器 (MAE).
  • 实现自主监督学习 (SSL) 与自主蒸 (SD) 进行增强的特征提取.
  • 开发了SSLSD-KTD方法,在其编码器和解码器中使用了本地和全球注意力机制.

主要成果:

  • 在KAUH-脏数据集上,SSLSD-KTD的准确度达到98.04%,在CT-脏数据集上达到82.14%.
  • 在相应的数据集上,转移学习提高了准确率至99.82%和95.24%.
  • 该方法证明了从有限的数据中有效地提取特征.

结论:

  • 该SSLSD-KTD方法显示出有很大的潜力来帮助甚至取代放射科医生在脏瘤诊断.
  • 这种方法在提取脏瘤特征方面是有效的,即使使用有限的数据集.
  • 这种人工智能驱动的工具可以提高瘤学的诊断准确性和效率.