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相关概念视频

Classification of Illness01:17

Classification of Illness

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
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Ultrasonography01:17

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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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相关实验视频

Updated: Jun 23, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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UKSSL:医疗图像分类的基础知识基础半监督学习.

Zeyu Ren1, Xiangyu Kong1, Yudong Zhang1

  • 1University of Leicester LE1 7RH Leicester U.K.

IEEE open journal of engineering in medicine and biology
|June 20, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了UKSSL,这是一个半监督的医疗图像分析框架. UKSSL有效地使用未标记的数据来提高对有限的标记医疗图像的分类性能.

关键词:
深度学习是一种深度学习.图像的分类图像的分类.医疗图像分析分析自主监督学习学习半监督学习 半监督学习

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科学领域:

  • 医学图像分析 医学图像分析
  • 深度学习 (Deep Learning) 是一种深度学习.
  • 计算机视觉 计算机视觉

背景情况:

  • 医学图像分析从深度学习中受益,但在有限的标记数据下面临挑战.
  • 没有标签的医疗图像通常数量超过标签的图像,阻碍了模型培训.
  • 使用稀缺的标记数据开发高性能模型是一个关键的研究领域.

研究的目的:

  • 引入UKSSL,这是一个基础的基于知识的半监督框架.
  • 为了应对训练有效的医疗图像分类模型的挑战,使用有限的标记数据.
  • 利用未标记的数据来提高医学图像分析的性能.

主要方法:

  • 英国SSL框架包括两个组成部分:MedCLR用于从未标记的数据中表示特征,UKMLP用于用标记的数据进行微调.
  • MedCLR从大型未标记的医疗图像数据集中提取基本知识.
  • UKMLP利用提取的特征和有限的标记数据进行医学图像分类.

主要成果:

  • 在LC25000和BCCD数据集上,UKSSL只使用50%的标记数据实现了高性能.
  • 在LC25000上,UKSSL报告了精度,回忆,F1得分和98.9%的准确性.
  • 在BCCD上,UKSSL实现了94.3%的精度,94.5%的回忆,94.3%的F1得分和94.1%的准确性,超过了100%标记数据的监督方法.

结论:

  • UKSSL有效地从未标记的医疗图像数据集中提取有价值的基础知识.
  • 该框架在医疗图像分类中表现出卓越的性能,使用有限的标记数据.
  • 在医学深度学习中,UKSSL为数据稀缺场景提供了一个有希望的解决方案.