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

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...

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

Generative model-based fundus photography translation for enhanced cross-device consistency.

Scientific reports·2026
Same author

Radial outer retina reflectivity (RORR) sign in LAMP2-associated retinopathy.

Acta ophthalmologica·2026
Same author

Curvature-Conforming Nanostructured Encapsulation for Washable and Mechanically Reliable Fiber OLEDs.

ACS applied materials & interfaces·2026
Same author

Chamber-Specific Decellularized Extracellular Matrices Differentially Modulate Cardiomyocyte Subtypes to Drive Engineered Heart Tissue Development and Function.

Advanced healthcare materials·2026
Same author

MHY5456, an FXR Agonist, Ameliorates Hepatic Steatosis and Fibrosis in a Mouse Model of MASLD.

Biomolecules & therapeutics·2026
Same author

Effect of axiopulpal line angle design and cement space settings on seating accuracy of CAD/CAM ceramic inlays: an in vitro study.

Journal of dentistry·2026

相关实验视频

Updated: Jun 20, 2026

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

2.7K

使用有限数据增强自动化偏分类:使用StyleGAN2-ADA进行数据增强.

Jaehan Joo1, Sang Yoon Kim2, Donghwan Kim1

  • 1Department of Electronics Engineering, Pusan National University, Busan, Republic of Korea.

PloS one
|May 24, 2024
PubMed
概括

这项研究介绍了一种使用StyleGAN2-ADA的生成数据增强方法,以改进深度学习,以在有限的数据的情况下实现自动化偏诊断. 该技术显著提高了分类性能,并减少了过度装配.

更多相关视频

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
07:11

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

1.5K
Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders
05:49

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Published on: November 1, 2024

772

相关实验视频

Last Updated: Jun 20, 2026

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

2.7K
Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
07:11

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

1.5K
Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders
05:49

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Published on: November 1, 2024

772

科学领域:

  • 眼科医生 眼科 眼科
  • 计算机科学 计算机科学
  • 人工智能的人工智能

背景情况:

  • 设计用于医疗诊断的深度学习系统,例如自动化视诊断,往往受到严重限制的数据集的阻碍.
  • 传统的数据增强方法可能不足以解决医学成像中的极端数据稀缺问题.

研究的目的:

  • 提出和评估一种生成数据增强技术,以改进基于深度学习的自动化偏诊断系统.
  • 评估生成模型在克服数据限制和减轻过度拟合方面的有效性.

主要方法:

  • 实现了一个基于StyleGAN2-ADA架构的生成模型.
  • 使用两个分类器来评估视的分类性能.
  • 将拟议的生成方法与传统数据增强技术进行了比较.
  • 使用分类器性能验证生成样本,并探索它们与眼科医生诊断的相关性.

主要成果:

  • 与传统方法相比,生成数据增强技术显示出了相当大的性能提升.
  • 这种方法有效地缓解了深度学习模型培训中的过度匹配问题.
  • 生成样本被验证为超越标准指标的实用性,如FID.
  • 研究了眼科医生同意和生成模型性能之间的关系.

结论:

  • 基于生成模型的数据增强显著改善了在偏诊断极度数据稀缺的场景中的定量性能.
  • 这种技术提供了一种实际的解决方案,可以在训练数据有限的情况下提高深度学习模型的稳定性和准确性.