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

Aggregates Classification01:29

Aggregates Classification

381
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...
381
Classification of Illness01:17

Classification of Illness

7.9K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
7.9K
Methods of Classification and Identification01:28

Methods of Classification and Identification

187
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...
187

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

Enhancing quantum audio watermarking security through joint verification and certification.

Scientific reports·2026
Same author

A Convolutional-Transformer Residual Network for Channel Estimation in Intelligent Reflective Surface Aided MIMO Systems.

Sensors (Basel, Switzerland)·2025
Same author

Attention-Based Transfer Enhancement Network for Cross-Corpus EEG Emotion Recognition.

Sensors (Basel, Switzerland)·2025
Same author

HRMamba: Fusing Luminance Information for Remote Physiological Measurement in Varied Lighting Conditions.

IEEE journal of biomedical and health informatics·2025
Same author

Image harmonization and de-harmonization based on singular value decomposition (SVD) in medical domain.

Quantitative imaging in medicine and surgery·2025
Same author

BRPDNet: A BioRegion Prompt Distillation Network for Physiological Monitoring.

IEEE journal of biomedical and health informatics·2025

相关实验视频

Updated: Sep 10, 2025

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.3K

VariMix:用于解释医学图像分类的多样性引导数据混合框架

Xiangyu Xiong1, Yue Sun1, Xiaohong Liu2

  • 1Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, China.

Computer methods and programs in biomedicine
|August 21, 2025
PubMed
概括

VariMix是一个新的数据混合框架,通过使用绝对差异地图来解决合成图像中的标签不匹配,从而提高医学图像分类的准确性. 这种方法显著提高了医疗AI应用中的深度神经网络的概括性.

关键词:
数据多样性可解释性超平面标签不匹配混合物综合数据

更多相关视频

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors
06:32

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Published on: August 18, 2023

2.3K
Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
10:17

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

13.3K

相关实验视频

Last Updated: Sep 10, 2025

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.3K
Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors
06:32

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Published on: August 18, 2023

2.3K
Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
10:17

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

13.3K

科学领域:

  • 计算机科学
  • 人工智能
  • 医学成像

背景情况:

  • 深度神经网络需要数据增强以防止过度匹配和改善概括性.
  • 生成对抗网络 (GAN) 合成了现实的图像,但往往缺乏多样性,并有模两可的标签.
  • 目前基于突出区域的数据混合策略可能无法捕获所有诊断信息,导致标签不匹配.

研究的目的:

  • 通过数据增强技术解决医学图像分类中的标签不匹配问题.
  • 提出一个新的数据混合框架,提高合成医学图像的准确性和多样性.

主要方法:

  • 推出了VariMix,一种以品种为导向的数据混合框架.
  • 使用图像对图像 (I2I) GAN生成的绝对差异图 (ADM) 来解决标签不匹配问题.
  • 在训练样本之间实现双向混合操作,以增强数据合成.

主要成果:

  • 在SwinT V2的使用中,VariMix的精度高达99. 30% (CXR) 和94. 60% (Retinal).
  • ConvNeXt分类器实现了最高准确率:乳腺US为87.73%,CXR为99.28%,视网膜US为95.13%和母胎US为95.81%.
  • 医学专家的评估证实了I2I GAN提高医学图像分类准确性的潜力.

结论:

  • 在四个公共数据集上,VariMix的性能优于现有的基于GAN和Mixup的方法.
  • I2I GAN通过生成超平面差异图提供了医学图像分类的可解释性.
  • 拟议的方法为医学深度学习中的数据增强提供了一种卓越的方法.