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

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)01:15

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) is an advanced Nuclear Magnetic Resonance (NMR) technique specifically designed to detect and enhance the signals of low-abundance nuclei, such as carbon-13 and nitrogen-15, in small molecules. The fundamental principle behind INEPT is the transfer of polarization from a more abundant and highly polarizable nucleus, typically hydrogen-1, to the low-abundance nucleus of interest. This process effectively boosts the NMR signal of the...

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

Unified Temporal-Spectral-Spatial Modeling for Robust and Generalizable Motor Imagery Brain-Computer Interfaces.

Bioengineering (Basel, Switzerland)·2026
Same author

YOLOv9-Based Detection of Diseases in Poplar Trees Using Histogram Equalization and Computer Vision.

Sensors (Basel, Switzerland)·2026
Same author

PatientEase-Domain-Aware RAG for Rehabilitation Instruction Simplification.

Bioengineering (Basel, Switzerland)·2025
Same author

A Generative Expert-Narrated Simplification Model for Enhancing Health Literacy Among the Older Population.

Bioengineering (Basel, Switzerland)·2025
Same author

From Anatomy to Genomics Using a Multi-Task Deep Learning Approach for Comprehensive Glioma Profiling.

Bioengineering (Basel, Switzerland)·2025
Same author

Smart City Infrastructure Monitoring with a Hybrid Vision Transformer for Micro-Crack Detection.

Sensors (Basel, Switzerland)·2025
Same journal

Correction: Komatsu et al. Three-Dimensional Visualization and Detection of the Pulmonary Venous-Left Atrium Connection Using Artificial Intelligence in Fetal Cardiac Ultrasound Screening. <i>Bioengineering</i> 2026, <i>13</i>, 100.

Bioengineering (Basel, Switzerland)·2026
Same journal

Comparison of CO<sub>2</sub> Laser and Microdebrider in the Surgical Treatment of Pediatric Recurrent Respiratory Papillomatosis: A Retrospective Analysis.

Bioengineering (Basel, Switzerland)·2026
Same journal

Toward More Translational Tumor Models: Breast dECM-Based 3D Systems Capture Native Microenvironmental Cues.

Bioengineering (Basel, Switzerland)·2026
Same journal

Postural Stability Changes During the 4 Phases of the Half Squat: Kinematics Profile of the Center of Pressure and Center of Mass in High-Performance Weightlifters-A Pilot Study.

Bioengineering (Basel, Switzerland)·2026
Same journal

Definite Implant Position as Novel Readout for Effectiveness of Ridge Preservation Indicates to Beneficial Effect of Combined Treatment with Platelet-Rich Fibrin (PRF) and Xenogenic Biomaterial in Bone Regeneration.

Bioengineering (Basel, Switzerland)·2026
Same journal

Trueness and Precision of Intraoral Scanners for 3D-Printed Orthodontic Models with Attachments: An In Vitro Comparative Study.

Bioengineering (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jun 1, 2026

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy
08:49

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

Published on: August 1, 2022

3.7K

从像素到精密双流深度网络用于病理核细分

Rashid Nasimov1, Kudratjon Zohirov2, Adilbek Dauletov3

  • 1Department of Computer Engineering, Gachon University Sujeong-Gu, Seongnam-si 13120, Gyeonggi-Do, Republic of Korea.

Bioengineering (Basel, Switzerland)
|August 28, 2025
PubMed
概括
此摘要是机器生成的。

一个新的深度学习模型,双流超融合网络 (DS-HFN),在细胞病理图像中准确地细分细胞核. 这种计算病理学工具平衡了背景和精度, 改善了疾病诊断和生物标志物分析.

关键词:
生物医学图像处理病理学中的深度学习双流网络组织病理图像分析核的细分

更多相关视频

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

9.3K
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

2.9K

相关实验视频

Last Updated: Jun 1, 2026

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy
08:49

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

Published on: August 1, 2022

3.7K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

9.3K
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

2.9K

科学领域:

  • 计算病理学
  • 数字病理学
  • 生物医学图像分析

背景情况:

  • 精确的细胞核细分对于计算病理学至关重要,影响疾病诊断和生物标志物分析.
  • 现有的深度学习模型难以平衡全球环境与精确的边界检测, 特别是重叠或变形的核.

研究的目的:

  • 引入一种新的深度学习模型,即双流超融合网络 (DS-HFN),用于强大而准确的细胞核细分.
  • 解决将语义上下文和细微边界细节整合到病理学图像中的挑战.

主要方法:

  • DS-HFN使用双流编码器同时捕获语义和边缘焦点特征.
  • 一个以注意力驱动的超特征嵌入模块 (HFEM) 融合了这些特征.
  • 双解码器架构和梯度调整损失功能提高了结构精度.

主要成果:

  • 在所有评估指标中,DS-HFN在基准数据集 (TNBC,MoNuSeg) 中表现优于30个最先进的模型.
  • 与现有方法相比,该模型在核细分方面表现出更高的准确性.
  • DS-HFN也显示了计算成本的降低.

结论:

  • DS-HFN为数字病理学中精确的细胞核细分提供了强大的解决方案.
  • 该模型精确地划分细胞核对于临床诊断和生物标志物分析至关重要.
  • DS-HFN通过提高细分精度和效率来推进计算病理学领域.