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

The Nucleus01:32

The Nucleus

93.5K
The nucleus is a membrane-bound organelle that acts as a control center in a eukaryotic cell. It contains chromosomal DNA, which controls gene expression and precisely regulates the production of proteins within the cell. In contrast, the DNA inside the mitochondria and chloroplast only carries out functions that are specific to those organelles.
Arrangement of DNA within Nucleus
The regulation of gene expression inside the nucleus is dependent on many factors, including the DNA structure. The...
93.5K
Vision01:24

Vision

55.4K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
55.4K
Nuclear Localization Signals and Import01:46

Nuclear Localization Signals and Import

6.0K
Proteins targeted to the nucleus carry short stretches of amino acid sequences called the nuclear localization signal or NLS. Classical nuclear localization signals are of two types: monopartite and bipartite NLS. Monopartite classical NLS (cNLS) consists of a single cluster of 4-8 amino acids. Bipartite cNLS consists of two clusters of  2-3 amino acids and a 9-12 residue long proline-rich linker bridging the two clusters. Signal clusters are rich in positively charged amino acids such as...
6.0K

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

Nonlinear hydrothermal associations between coupled landscape ecological risk and resilience in a major grain-producing region of China.

Journal of environmental management·2026
Same author

LangSurf: Language-Embedded Surface Gaussians for 3D Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Breathing New Life into Small Object Detection with Detection-Oriented Rectification.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

A novel multi-task deep learning framework for classification and detection of intracranial structures in first-trimester fetal ultrasound images.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)·2026
Same author

PathTIGR: A pathway topology-informed graph representation learning framework for immunotherapy response prediction.

Science advances·2026
Same author

Interpretable graph deep learning framework for drug synergy prediction by integrating functional and clinical similarities.

NPJ digital medicine·2026
Same journal

LLM-enhanced Neuron Segmentation and Reconstruction in Complex Mouse Brain Images.

IEEE transactions on medical imaging·2026
Same journal

Matrixed-Spectrum Decomposition Accelerated Linear Boltzmann Transport Equation Solver for Fast Scatter Correction in Multi-Spectral CT.

IEEE transactions on medical imaging·2026
Same journal

The Ritz Adjoint Method for MRI Pulse Design.

IEEE transactions on medical imaging·2026
Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026
Same journal

Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

IEEE transactions on medical imaging·2026
Same journal

4D Reconstruction of Fetal Left Ventricle from Echocardiography via 2.5D Radial Segmentation and Graph-Fourier Reconstruction.

IEEE transactions on medical imaging·2026
查看所有相关文章

相关实验视频

Updated: Sep 18, 2025

Using Computer Vision Libraries to Streamline Nuclei Quantification
06:25

Using Computer Vision Libraries to Streamline Nuclei Quantification

Published on: June 6, 2025

428

推动视觉语言模型用于核心实例分割和分类.

Jieru Yao, Guangyu Guo, Zhaohui Zheng

    IEEE transactions on medical imaging
    |June 25, 2025
    PubMed
    概括
    此摘要是机器生成的。

    PromptNu通过利用视觉语言模型 (VLM) 和提示工程来增强整个幻灯片成像 (WSI) 中的核实例细分和分类. 这种新的框架减少了对准确核检测的手动注释的依赖.

    更多相关视频

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    3.0K
    Exploiting Live Imaging to Track Nuclei During Myoblast Differentiation and Fusion
    09:03

    Exploiting Live Imaging to Track Nuclei During Myoblast Differentiation and Fusion

    Published on: April 13, 2019

    8.3K

    相关实验视频

    Last Updated: Sep 18, 2025

    Using Computer Vision Libraries to Streamline Nuclei Quantification
    06:25

    Using Computer Vision Libraries to Streamline Nuclei Quantification

    Published on: June 6, 2025

    428
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    3.0K
    Exploiting Live Imaging to Track Nuclei During Myoblast Differentiation and Fusion
    09:03

    Exploiting Live Imaging to Track Nuclei During Myoblast Differentiation and Fusion

    Published on: April 13, 2019

    8.3K

    科学领域:

    • 数字病理学数字病理学
    • 计算生物学 计算生物学
    • 人工智能的人工智能

    背景情况:

    • 核实例细分和分类在整个幻灯片成像 (WSI) 分析中至关重要,但具有挑战性.
    • 当前的方法通常需要大量的手动注释,需要大量的时间和专业知识.
    • 视觉语言模型 (VLMs) 通过从大规模的图像文本数据中学习,而无需繁的标签,提供了一个有希望的替代方案.

    研究的目的:

    • 引入PromptNu,这是一个用于核实例识别的新框架.
    • 在模型培训中注入全面的核心知识,使用视觉语言对比学习和提示工程.
    • 在WSI分析中克服传统注释依赖方法的局限性.

    主要方法:

    • 开发了多方面的提示,整合了视觉,统计和专家核知识.
    • 提议促进核表示学习 (PNuRL) 用于特征提取.
    • 引入了促进核密度预测 (PNuDP) 以整合VLM专业知识用于预测.
    • 利用视觉语言对比学习来增强核实例识别.

    主要成果:

    • 在六个不同的数据集中证明了PromptNu的有效性.
    • 在核实例细分和分类任务中取得了显著的改进.
    • 在各种整个幻灯片成像场景中验证了框架的性能.

    结论:

    • 在WSI中,PromptNu成功地整合了VLM知识和用于核分析的Prompt工程.
    • 拟议的方法提供了一个高效和有效的替代手动注释驱动的方法.
    • 该框架显示了推进自动化数字病理学的巨大潜力.