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

Skin Cancer01:30

Skin Cancer

5.7K
Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...
5.7K
Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

59.3K
Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...
59.3K
Classification of Connective Tissues01:30

Classification of Connective Tissues

14.5K
The connective tissues have different properties and functions in the human body. They are broadly categorized into proper, supporting, or fluid connective tissues.
Connective Tissue Proper
Connective tissue proper is the most abundant class of connective tissues. As its name implies, it predominantly connects different tissues in the body. Depending on the cell types, ground substance, viscosity, and fiber types in the ECM, connective tissue proper is further categorized into loose and dense....
14.5K

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

Novel online fabric color difference detection system based on machine vision.

Scientific reports·2026
Same author

CWAGS: multi-trait genomic selection using channel weighted attention convolutional network.

BMC genomics·2026
Same author

Levels and determinants of child wasting relapse: a prospective cohort study from Somalia.

Journal of global health·2026
Same author

Human reconstruction using 3D Gaussian Splatting: a brief survey.

Frontiers in artificial intelligence·2025
Same author

Integrating convolutional and transformer networks for precise diagnosis of watershed and hemorrhagic stroke.

Scientific reports·2025
Same author

Evaluating the Performance of Integrated Management of Acute Malnutrition Programs in Somalia: A Systematic Review and Meta-Analysis.

International journal of environmental research and public health·2025

相关实验视频

Updated: Jan 11, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K

使用基于VGG19的特征编码进行变压器辅助皮肤癌分类.

Fallah H Najjar1,2, Zaid Nidhal Khudhair3,4, Farhan Mohamed5,6

  • 1Department of Emergent Computing, Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, 81310, Johor, Malaysia. fallahnajjar@atu.edu.iq.

Scientific reports
|November 17, 2025
PubMed
概括
此摘要是机器生成的。

一个新的混合深度学习模型VGG19-RSPDA-ViT通过结合本地和全球特征来改善皮肤癌的检测. 这种方法提高了诊断的准确性和概括性,为自动化皮肤病学提供了一个有前途的工具.

关键词:
图像增强 图像增强 图像增强皮肤癌是一种皮肤癌.皮肤病变的分类 皮肤病变的分类在VGG19-RSPDA-ViTT中.

更多相关视频

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
06:08

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

17.2K

相关实验视频

Last Updated: Jan 11, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K
Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
06:08

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

17.2K

科学领域:

  • 皮肤病学和人工智能研究
  • 医学成像和诊断 医学成像和诊断

背景情况:

  • 皮肤癌是全球主要的健康问题,需要准确和早期诊断,以改善患者的治疗结果.
  • 深度学习模型,特别是卷积神经网络 (CNN),对自动化皮肤病变分类有希望,但在数据集依赖性,定向灵敏性和全球上下文建模方面存在局限性.

研究的目的:

  • 开发和评估混合深度学习模型VGG19-RSPDA-ViT,用于增强皮肤病变分类.
  • 通过整合细粒度的本地特征提取与全球上下文建模和旋转不变度来解决现有的CNN的局限性.

主要方法:

  • 提出了一个混合模型,将VGG19用于本地特征和视觉转换器 (ViT) 用于全球环境.
  • 引入了基于旋转和转移的数据增强 (RSPDA) 技术,以强制执行旋转不变性和丰富特征表示,改善对小数据集的概括性.
  • 该模型的性能在三个基准数据集上得到验证:MSK10000 (二进制分类),HAM10000和PH2 (多类分类).

主要成果:

  • VGG19-RSPDA-ViT模型实现了高精度:97.9%在MSK10000,97.1%在HAM10000上,以及98.67%在PH2.2上.
  • 该模型在所有测试的数据集中始终表现出高的宏观平均精度,回忆,特异性和F1分数.
  • 与现有的最先进的方法相比,观察到更高的概括能力.

结论:

  • 拟议的VGG19-RSPDA-ViT模型有效地对皮肤病变进行分类,性能优于目前的方法.
  • 将CNN和变形机与新增增强策略的整合显示出作为皮肤病学中自动诊断工具的临床应用的巨大潜力.