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

Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

7.0K
Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
7.0K

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

CystoDS: a multiclass endoscopy image dataset for artificial intelligence-assisted bladder cancer detection.

Scientific data·2026
Same author

Deep radiomics for prognostic prediction in locally advanced non-small cell lung cancer by leveraging OmicsMap-based image representation.

Physics in medicine and biology·2026
Same author

Revealing neurocognitive and behavioral patterns through unsupervised manifold learning of dynamic brain data.

Nature computational science·2025
Same author

Restoring Mitochondrial Quantity and Quality to Reverse Warburg Effect and Drive Tumor Differentiation.

Research square·2024
Same author

Deep representation learning of protein-protein interaction networks for enhanced pattern discovery.

Science advances·2024
Same author

Interpretable discovery of patterns in tabular data via spatially semantic topographic maps.

Nature biomedical engineering·2024

相关实验视频

Updated: Jul 15, 2025

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol
10:14

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

Published on: May 12, 2019

7.3K

超高分辨率的生物医学成像通过无引用的统计隐性神经表征.

Siqi Ye1, Liyue Shen2, Md Tauhidul Islam1

  • 1Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, United States of America.

Physics in medicine and biology
|September 27, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的无监督深度学习方法,用于生物医学图像超分辨率 (SR). 统计隐性神经表示 (INR) 框架从有限的低分辨率数据中生成高质量的SR图像,而不需要配对的示例.

关键词:
生物医学成像成像技术隐含的神经表现隐含的神经表现反向问题反向问题最大的概率估计估计.多尺度成像多尺度成像超级分辨率的超级分辨率没有监督的学习学习.

更多相关视频

Two-Dimensional Super-Resolution Visualization of Rat Brain Microvasculature Using Ultrasound Localization Microscopy
07:33

Two-Dimensional Super-Resolution Visualization of Rat Brain Microvasculature Using Ultrasound Localization Microscopy

Published on: March 28, 2025

447
A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells
12:49

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Published on: September 28, 2019

12.8K

相关实验视频

Last Updated: Jul 15, 2025

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol
10:14

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

Published on: May 12, 2019

7.3K
Two-Dimensional Super-Resolution Visualization of Rat Brain Microvasculature Using Ultrasound Localization Microscopy
07:33

Two-Dimensional Super-Resolution Visualization of Rat Brain Microvasculature Using Ultrasound Localization Microscopy

Published on: March 28, 2025

447
A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells
12:49

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Published on: September 28, 2019

12.8K

科学领域:

  • 生物医学成像技术 生物医学成像技术
  • 深度学习 (Deep Learning) 是一种深度学习.
  • 图像处理 图像处理

背景情况:

  • 图像超分辨率 (SR) 的监督深度学习面临生物医学成像方面的挑战,原因是培训的配对低分辨率 (LR) 和高分辨率 (HR) 图像稀缺.
  • 现有的方法通常需要大量的数据集,这限制了它们在专门的医学成像场景中的适用性.

研究的目的:

  • 开发一个无参考,无监督的深度学习框架,用于生成高质量的生物医学SR图像.
  • 通过只使用单个或少数观察到的LR图像来解决监督SR方法的局限性.

主要方法:

  • 提出了一个统计隐性神经表示 (INR) 框架,使用最大概率估计建模LR图像统计.
  • 一个INR网络,一个以坐标为基础的多层感知器,被训练来表示潜伏的HR图像作为连续的空间函数.
  • 该方法确保了功能流性,并支持SR成像的任意缩放.

主要成果:

  • 该框架的有效性在各种生物医学成像模式上得到验证,包括CT,MRI,光显微镜和超声波.
  • 通过使用有限的LR数据,在各种放大尺度 (2×,4×,8×) 实现了成功的SR图像生成.
  • 拟议的统计INR方法证明了其对高质量的SR重建的潜力.

结论:

  • 开发的无监督深度学习框架为缺乏HR参考数据的生物医学SR应用提供了重大进展.
  • 这种无参考的统计INR方法为医学成像中的众多SR任务提供了可行的解决方案.
  • 该方法克服了生物医学SR监督学习固有的数据限制.