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

An Explainable Transformer-Based Framework for Lung Cancer Classification and Automated Radiology Report Generation from Multi-Slice CT Images.

Biomedicines·2026
Same author

Prognostic Value of Left Ventricular Kinetic Energy for Predicting Major Adverse Events in Hypertrophic Cardiomyopathy.

JACC. Asia·2026
Same author

LymphUs: A multicenter open-access database of lymph node ultrasound images in patients with papillary thyroid carcinoma for clinical and artificial intelligence research.

Data in brief·2026
Same author

Skeletal and cardiac muscle longitudinal associations in the Baltimore Longitudinal Study of Aging (BLSA).

BMC medicine·2026
Same author

Coronary CT Angiography-Based Prediction Model for Hemodynamically Significant Coronary Stenosis Integrating Morphological and Plaque Characteristics.

Cardiovascular engineering and technology·2026
Same author

Variations in Frailty Perceptions between Patients and Physicians.

Gerontology·2026

相关实验视频

Updated: Jun 6, 2025

Author Spotlight: Enhancing Multicolor Fluorescence Localization in Lung Carcinoma Sample
05:00

Author Spotlight: Enhancing Multicolor Fluorescence Localization in Lung Carcinoma Sample

Published on: November 21, 2023

1.7K

一个新的混合模型,用于自动非小细胞肺癌分类,使用 histopathological 图像.

Oguzhan Katar1, Ozal Yildirim1, Ru-San Tan2,3

  • 1Department of Software Engineering, Firat University, Elazig 23119, Turkey.

Diagnostics (Basel, Switzerland)
|November 27, 2024
PubMed
概括

一个新的混合模型使用深度,纹理和上下文特征从组织病理图像中准确地分类非小细胞肺癌 (NSCLC) 亚型,达到99.87%的准确性. 这有助于病理学家在诊断和治疗规划.

关键词:
自动化诊断自动化诊断特性提取 特性提取基因病理学图像 基因病理学图像肺癌是一种肺癌.视觉变压器 视觉变压器

更多相关视频

Pathological Analysis of Lung Metastasis Following Lateral Tail-Vein Injection of Tumor Cells
08:54

Pathological Analysis of Lung Metastasis Following Lateral Tail-Vein Injection of Tumor Cells

Published on: May 20, 2020

8.9K
Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques
09:48

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

7.4K

相关实验视频

Last Updated: Jun 6, 2025

Author Spotlight: Enhancing Multicolor Fluorescence Localization in Lung Carcinoma Sample
05:00

Author Spotlight: Enhancing Multicolor Fluorescence Localization in Lung Carcinoma Sample

Published on: November 21, 2023

1.7K
Pathological Analysis of Lung Metastasis Following Lateral Tail-Vein Injection of Tumor Cells
08:54

Pathological Analysis of Lung Metastasis Following Lateral Tail-Vein Injection of Tumor Cells

Published on: May 20, 2020

8.9K
Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques
09:48

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

7.4K

科学领域:

  • 在瘤学瘤学.
  • 医疗成像医学成像
  • 计算病理学计算病理学

背景情况:

  • 肺癌,特别是非小细胞肺癌 (NSCLC),是癌症死亡的主要原因.
  • 准确的NSCLC亚型分类对于有效的治疗策略至关重要.
  • 手动组织病理图像分析需要大量的时间和专业知识.

研究的目的:

  • 从基因病理图像开发一种混合模型,用于自动化NSCLC亚型分类.
  • 为了提高NSCLC诊断的准确性和效率.

主要方法:

  • 采用了一种混合模型,集成了EfficientNet-B0 (深度特征),局部二进制模式 (LBP,纹理特征) 和视觉转换器 (ViT,上下文特征).
  • 提取的特征被组合成一个全面的向量,用于机器学习分类器.
  • 评估了支持向量机 (SVM),物流回归 (LR),LightGBM和XGBoost.

主要成果:

  • 使用混合模型与EfficientNet-B0,LBP,ViT编码器和SVM实现了99.87%的最高分类准确率.
  • 与传统方法相比,拟议的模型显著提高了NSCLC亚型分类的准确性.
  • 测试了多个训练场景和分类器,以验证模型的性能.

结论:

  • 混合模型有效地整合了各种功能,使得NSCLC的分类更加稳定.
  • 这种方法提高了诊断的准确性,降低了误诊的风险,并支持更好的治疗规划.
  • 在临床病理学中,NSCLC亚型的自动分类显示出显著的前景.