Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Reasoning in machine vision by learning fast and slow thinking.

Nature communications·2026
Same author

Development and validation of a versatile foundation model for cine cardiac magnetic resonance image analysis.

Communications medicine·2026
Same author

Cascaded Deep Learning-Based Model for Classification and Segmentation of Plaques from Carotid Ultrasound Images.

Bioengineering (Basel, Switzerland)·2026
Same author

On the degrees of freedom of gridded control points in learning-based medical image registration.

Medical physics·2026
Same author

A computationally frugal, open-source chest CT foundation model for thoracic disease detection in lung cancer screening programmes.

Communications medicine·2026
Same author

CoV-UniBind: a unified antibody binding database for SARS-CoV-2.

Bioinformatics advances·2026

相关实验视频

Updated: Jun 20, 2026

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
08:40

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

12.7K

一个半监督的前列腺损伤细分的原型网络从多模式MRI.

Wen Yan1,2, Yipeng Hu2, Qianye Yang2

  • 1Department of Electrical Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Hong Kong Special Administrative Region of China, People's Republic of China.

Physics in medicine and biology
|March 17, 2025
PubMed
概括

一个新的半监督算法通过在平均教师培训中使用原型学习来增强前列腺损伤细分,提高了有限的标记数据的准确性,以便做出更好的临床决策.

关键词:
前列腺病变细分的细分.一个原型的算法.半监督方法 半监督方法

更多相关视频

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

47.7K
A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
06:08

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

114

相关实验视频

Last Updated: Jun 20, 2026

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
08:40

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

12.7K
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

47.7K
A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
06:08

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

114

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 多参数MRI的前列腺病变细分受到有限的注释数据的阻碍.
  • 由于数据稀缺,监督模型难以学习复杂的特征以准确检测和细分病变.

研究的目的:

  • 开发一种新的半监督算法,以改善前列腺损伤细分.
  • 用原型学习和平均教师培训来增强未标记数据的特征表示.

主要方法:

  • 嵌入式原型学习到平均教师 (MT) 培训中,用于半监督的细分.
  • 利用来自教师网络的伪标签用于未标记的原型基于细分.
  • 在标记和未标记的数据中,支持和查询路径之间启用了双向原型流.

主要成果:

  • 拟议的算法在多机构数据集上胜过了最先进的半监督方法.
  • 通过增加标记数据,实现了改善的子相似度系数 (0.04-0.09).
  • 在PROSTATEx/PROSTATEx2数据集上表现出强的表现.

结论:

  • 这种新型的半监督方法在有限的标记数据下显著改善前列腺病变细分的前景.
  • 这种方法有可能帮助临床医生在患者治疗和管理决策中.
  • 算法实现在GitHub上公开提供.