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

6.9K
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...
6.9K
Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

4.6K
Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
4.6K
Confocal Fluorescence Microscopy01:16

Confocal Fluorescence Microscopy

13.1K
Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...
13.1K

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

Free-Space-Coupled Frequency-Locked Microtoroid Resonators with Reactive Polymer Functionalization for Part-Per-Trillion Gas Detection.

Laser & photonics reviews·2026
Same author

The Patient Experience of Cancer-Related Cachexia: Implications for Patient-Reported Outcomes Measures.

Clinical Medicine Insights. Oncology·2026
Same author

Real-world prescribing in accordance to ACC/AHA guidelines for lipid-lowering therapy in high-risk primary and secondary prevention of ASCVD: Real-World Prescribing for Lipid-Lowering Therapy.

American journal of preventive cardiology·2025
Same author

Building blocks for nanophotonic devices and metamaterials.

Chemical communications (Cambridge, England)·2025
Same author

Fast and accurate electromagnetic field calculation for substrate-supported metasurfaces using the discrete dipole approximation.

Nanophotonics (Berlin, Germany)·2024
Same author

Reply to Comment on "Fast and accurate electromagnetic field calculation for substrate-supported metasurfaces using the discrete dipole approximation".

Nanophotonics (Berlin, Germany)·2024

相关实验视频

Updated: Jun 16, 2025

Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization
10:28

Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization

Published on: July 5, 2016

10.3K

深度学习优化用于在无透镜全息显微镜中对小物体进行分类.

Colin J Potter, Shriniketh Sreevatsan, Euan McLeod

    Optics express
    |June 14, 2025
    PubMed
    概括
    此摘要是机器生成的。

    这项研究探讨了浅层卷积神经网络,用于在无镜头全息显微镜传感器中对小物体进行分类. 研究发现,激活层显著影响分类准确性,达到大约83%.

    更多相关视频

    Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution
    08:41

    Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

    Published on: August 16, 2012

    11.5K
    Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
    10:16

    Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

    Published on: February 8, 2014

    12.2K

    相关实验视频

    Last Updated: Jun 16, 2025

    Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization
    10:28

    Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization

    Published on: July 5, 2016

    10.3K
    Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution
    08:41

    Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

    Published on: August 16, 2012

    11.5K
    Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
    10:16

    Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

    Published on: February 8, 2014

    12.2K

    科学领域:

    • 生物医学工程 生物医学工程
    • 计算生物学 计算生物学
    • 光学和光子学 在光学和光子学.

    背景情况:

    • 无镜头全息显微镜提供高分辨率,大视野成像.
    • 通过神经网络进行自动化图像处理,通过使用标记的微型和纳米珠来增强生物分子传感.
    • 在全息显微镜中对小物体进行分类的最佳神经网络架构仍未得到充分探索.

    研究的目的:

    • 为了研究浅层卷积神经网络在无透镜全息显微镜中用于小物体分类的性能.
    • 分析各种网络层和超参数对分类准确性的影响.

    主要方法:

    • 浅卷积神经网络的应用,用于对全息显微镜图像中的小物体进行分类.
    • 系统评估层 (掉落,卷积,正常化,聚合,激活) 和超参数 (掉落分数,过器数量/大小,步幅,填充).

    主要成果:

    • 获得了大约83%的分类准确度.
    • 确定激活层是最大限度地提高准确性的最关键因素.
    • 证明了卷积神经网络对于这个特定的分类任务的有效性.

    结论:

    • 浅卷积神经网络适合在无透镜全息显微镜中对小物体进行分类.
    • 精心选择网络架构,特别是激活层,对于优化传感器性能至关重要.
    • 这些发现为在类似的全息传感应用中开发神经网络提供了指导.