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

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

Influence of eco-friendly float replacement on microplastic pollution and their metal adsorption behavior in Sanggou Bay, China.

Marine environmental research·2025
Same author

The timing of using IVIG for neonatal ABO hemolytic disease.

BMC pediatrics·2025
Same author

SFTSV Prevalence in Ticks and Livestock in an SFTSV-Endemic Area in Central China.

Pathogens (Basel, Switzerland)·2025
Same author

RNA interference-mediated silencing of hypoxia-inducible factor 1 signaling pathway genes enhanced the hypoxia sensitivity in the brown-legged grain mite, Aleuroglyphus ovatus.

Pest management science·2025
Same author

Waterborne and dietary accumulation and toxicity of iron-based metal-organic framework in cladoceran Moina mongolica Daday.

Journal of hazardous materials·2025
Same author

Low-concentration lead stress enhanced the reproductive potential of Aleuroglyphus ovatus (Troupeau) (Acarina: Acaridae) by upregulating both the expression level and protein level of Vitellogenin gene.

Experimental & applied acarology·2025
Same journal

A computational model of chemically- and mechanically-induced thrombus formation in cerebral aneurysms.

Computers in biology and medicine·2026
Same journal

An improved catch fish optimization based deep learning model for Parkinson disease classification using EEG signal.

Computers in biology and medicine·2026
Same journal

Assessing the robustness of evaluation metrics for synthetic ECG signal quality.

Computers in biology and medicine·2026
Same journal

Integrating stemness and epithelial-mesenchymal transition signatures with machine learning identifies RUNX1 as a therapeutic vulnerability in colorectal cancer.

Computers in biology and medicine·2026
Same journal

Differential regional textural attributes of tongue in normal and acidity patients in the light of traditional Chinese medicine.

Computers in biology and medicine·2026
Same journal

SC-MSDNet: Spatial-consistent multi-view self-distillation for retinal OCT classification.

Computers in biology and medicine·2026
查看所有相关文章

相关实验视频

Updated: Jul 6, 2025

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

2.8K

弱监督的图像分割超出了严格的界限框注释.

Juan Wang1, Bin Xia2

  • 1Horizon Med Innovation Inc., 23421 South Pointe Dr., Laguna Hills, CA 92653, USA.

Computers in biology and medicine
|January 4, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的弱监督图像分割方法,使用基于极转换的多个实例学习 (MIL). 它甚至在宽松的界限框注释的情况下也实现了最先进的性能,克服了以前方法的局限性.

关键词:
界限盒子 界限盒子 界限盒子深度神经网络是一种深度神经网络.多个实例的学习是多个实例的学习.极地转换的极地转换弱监督的图像细分是图像细分的弱监督

更多相关视频

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

405
Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

9.8K

相关实验视频

Last Updated: Jul 6, 2025

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

2.8K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

405
Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

9.8K

科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 图像分析 图像分析

背景情况:

  • 弱监督的图像分割通常需要精确的 (紧密的) 界限框注释来实现高性能.
  • 与较宽松的注释相比,获得紧密的边界框是劳动密集型和具有挑战性的.
  • 当前的方法在使用不那么精确的界限框监督时,难以保持细分精度.

研究的目的:

  • 调查只使用松散界限框监督实现高图像分割性能的可行性.
  • 开发一种新的弱监督图像细分方法,这种方法对注释精度具有强大影响.
  • 扩展先前的多实例学习 (MIL) 技术,以改善细分,具有多样化的界限框密度.

主要方法:

  • 集成基于极转换的MIL策略与现有的平行转换MIL用于图像细分.
  • 积极袋的定义是像素沿着极线在边界框内,帮助对象定位.
  • 引入加权的平滑最大近似来优先考虑靠近极转换原点的像素.

主要成果:

  • 拟议的方法在各种界限框精度级别中展示了最先进的性能.
  • 该方法在松散的界限框注释中显示了对轻度和中度不准确性的稳定性.
  • 使用Dice系数对两个公共数据集的评估证实了极转换MIL战略的有效性.

结论:

  • 弱监督的图像分割可以使用宽松的边界框实现高性能,并采用拟议的极转换MIL方法.
  • 该方法有效地解决了在图像分割任务中获得精确注释的实际挑战.
  • 这项工作通过在不那么严格的监管条件下实现强大而准确的细分来推动该领域的发展.