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

Irritable Bowel Syndrome: Contemporary Management Approaches, Limitations, and Future Directions.

Cureus·2026
Same author

A lightweight hybrid framework for real-time data refinement in resource-constrained underwater and underground wireless sensor networks.

Scientific reports·2026
Same author

Emergence and genomic characterization of Listeria monocytogenes causing human listeriosis in Pakistan, 2017-2023.

BMC microbiology·2026
Same author

predALZ: An Ensemble Learning Framework for Identifying Genetic Biomarkers in Familial Alzheimer's Disease.

Current drug targets·2026
Same author

Multimodal healthcare system for human activity recognition using multiple features and advanced ensemble classifier.

Digital health·2026
Same author

HybridTrust: on-device federated learning with crypto-agile security for legacy and quantum-safe medical devices.

Scientific reports·2026

相关实验视频

Updated: Jul 12, 2025

A Permanent Window for Investigating Cancer Metastasis to the Lung
07:06

A Permanent Window for Investigating Cancer Metastasis to the Lung

Published on: July 1, 2021

4.9K

肺癌分类在组织病理学图像使用多分辨率高效网.

Sunila Anjum1, Imran Ahmed2, Muhammad Asif3

  • 1Center of Excellence in Information Technology, Institute of Management Sciences, Hayatabad, Peshawar 25000, Pakistan.

Computational intelligence and neuroscience
|October 25, 2023
PubMed
概括

这项研究引入了一种使用EfficientNet变体的自动深度学习方法,以在组织病理图像中对癌症进行分类. EfficientNetB2实现了97%的准确性,为病理学家提供了更快,更精确的诊断工具.

更多相关视频

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.5K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

相关实验视频

Last Updated: Jul 12, 2025

A Permanent Window for Investigating Cancer Metastasis to the Lung
07:06

A Permanent Window for Investigating Cancer Metastasis to the Lung

Published on: July 1, 2021

4.9K
Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.5K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

科学领域:

  • 数字病理学和计算成像.
  • 医疗诊断中的人工智能.
  • 癌症研究和组织病理学分析.

背景情况:

  • 组织病理学图像对于疾病诊断至关重要,包括癌症.
  • 数字组织病理学提高了诊断精度和病理学家的效率.
  • 手动图像分析耗时,需要自动化解决方案.

研究的目的:

  • 开发和评估一种深度学习方法,用于对正常和恶性癌症组织病理图像进行自动分类.
  • 为了比较这个分类任务的EfficientNet变体 (B0-B7) 的性能.
  • 调查不同图像分辨率的影响,并将学习转移到模型准确度.

主要方法:

  • 应用EfficientNet深度学习模型 (B0-B7) 用不同的图像分辨率 (224x224到600x600像素).
  • 利用转移学习和参数调整来优化模型性能和减轻过度拟合.
  • 采用了肺癌和结肠癌组织病理图像LC25000数据集 (五个类别的25000张图像).
  • 执行数据预处理以确保图像质量和标准化.

主要成果:

  • 所有的EfficientNet变体都显示出很好的分类准确性.
  • EfficientNetB2使用260x260像素图像实现了97%准确度的出色性能.
  • 这项研究证实了深度学习对组织病理图像分析的有效性.

结论:

  • 深度学习,特别是EfficientNet模型,提供了一种可靠的自动化方法,用于在组织病理图像中对癌症进行分类.
  • EfficientNetB2在数字病理学中改善诊断速度和准确性方面显示出显著的前景.
  • 自动化分析可以通过实现更快的治疗干预来提高治疗结果.