Jove
Visualize
联系我们

相关概念视频

Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...
Methods of Classification and Identification01:28

Methods of Classification and Identification

Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...

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 authorSame journal

Deep learning-driven super-resolution for cone-beam computed tomography: An <i>ex vivo</i> proof-of-concept study using artificially degraded micro-computed tomography data.

Imaging science in dentistry·2026
Same author

Generative Artificial Intelligence for Computer Vision in Endodontics: A Review of Current State and Future Potential.

International endodontic journal·2026
Same author

Application of Artificial Intelligence in Detecting Dental Anomalies: Current Models, Imaging Modalities, and Future Directions.

Health science reports·2026
Same author

Efficacy of Automatic 3D Segmentation of the Upper Airway in CBCT or CT Scans via Artificial Intelligence Versus Manual Segmentation by Human Experts: A Systematic Review and Meta-Analysis.

Clinical and experimental dental research·2026
Same author

Effect of Voxel Size on Cone-Beam Computed Tomography-Based Assessment of Root Canal Anatomy: A Systematic Review and Meta-Analysis.

International endodontic journal·2026
Same author

Research That Matters: A Call for Enhancing Rigour and Relevance in Artificial Intelligence Research in Endodontics.

International endodontic journal·2026
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

相关实验视频

Updated: Jun 16, 2026

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

1.1K

使用多阶段深度学习方法提高宫成熟度分类的准确性.

Parisa Motie1, Ali Ashkan2, Hossein Mohammad-Rahimi3,4

  • 1Medical Image and Signal Processing Research Center, Medical University of Isfahan, Isfahan, Iran.

Imaging science in dentistry
|October 10, 2025
PubMed
概括
此摘要是机器生成的。

这项研究开发了一个自动化框架来分类椎成熟 (CVM) 阶段,实现了有希望的准确性. 这种人工智能驱动的方法有助于预测正治疗规划的生长模式.

关键词:
人工智能的人工智能宫脊椎 宫脊椎 宫脊椎 宫脊椎分类 分类 分类 分类.深度学习 (Deep Learning) 是一种深度学习.增长的增长 增长的增长

相关实验视频

Last Updated: Jun 16, 2026

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

1.1K

科学领域:

  • ортодонтика和牙科成像 牙科成像
  • 医疗保健中的人工智能
  • 生物识别分析 生物识别分析

背景情况:

  • 准确地分类椎成熟 (CVM) 阶段对于预测形牙中生长冲击和模式至关重要.
  • 手动CVM评估依赖于横向脑图的主观解释,需要客观和自动化的方法.
  • 开发一个自动化系统可以提高正义牙科诊断的效率和一致性.

研究的目的:

  • 开发和评估一个多阶段的,用于分类椎成熟 (CVM) 阶段的自动化框架.
  • 通过使用深度学习模型,提高CVM评估的精度和可靠性.
  • 为提供一个工具,以更准确地预测正牙患者的生长速度和模式.

主要方法:

  • 使用了2325个侧向脑图的数据集,专家将其分为6个CVM阶段.
  • 实施了两阶段的深度学习方法:对象检测 (Faster RCNN) 用于区域提取和分类 (ResNet 101) 用于CVM分期.
  • 模型使用10倍交叉验证进行训练和验证,并通过梯度加权类激活地图对学习过程进行可视化.

主要成果:

  • 整体自动化框架在CVM分类方面实现了有希望的82.96%的准确性.
  • 用于感兴趣区域提取的物体检测显示出高性能,mAP50和mAP75值为100%.
  • 区分CS1-CS3和CS4-CS6阶段的初始分类模型达到99.10%的准确率;随后的分类显示准确率为86.49% (CS1-CS3) 和82.80% (CS4-CS6).

结论:

  • 开发的CVM分类的全自动化多阶段框架显示出有希望的准确性.
  • 这种人工智能驱动的方法为手动CVM评估提供了可靠和高效的替代方案.
  • 进一步完善自动化框架可能会显著有利于正牙治疗规划和生长预测.