Search research articles

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 Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

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

Detection of acute lymphocytic and myeloid leukaemia with optimized dimensionality reduction and deep learning classification framework.

Current research in translational medicine·2026

Same author

Bridging modalities: a deep learning framework for brain tumor classification via CT-MRI integration and model fusion.

Frontiers in computational neuroscience·2026

Same author

A hybrid optimized framework with energy shape prior segmentation for brain tumor detection in MRI images.

Digital health·2026

Same author

Integrative multi-stage deep learning framework for ovarian tumor ultrasound classification with explainability and confidence estimation.

Frontiers in medicine·2026

Same author

Quantum transfer learning for cross-domain cybersecurity threat detection and categorization.

Scientific reports·2026

Same author

Deep transfer learning and explainable AI framework for autism spectrum disorder detection across multiple datasets.

Frontiers in neurology·2026

Search research articles

相关实验视频

Updated: Jun 27, 2025

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

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

Published on: October 10, 2018

基于转移学习的方法用于肺癌和结肠癌检测,使用本地二进制模式特征和可解释的人工智能 (AI) 技术.

Shtwai Alsubai¹

¹Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia.

PeerJ. Computer science

|April 25, 2024

概括

这项研究引入了一个先进的Inception-ResNetV2模型,具有局部二进制模式 (LBP),用于增强肺癌和结肠癌诊断. 这种机器学习方法达到99.98%的准确性,承诺更快,更可靠的癌症检测.

关键词:

大肠癌是什么意思大肠癌是什么意思局部二进制模式的特征是局部二进制模式的特征.肺癌是一种肺癌.转移学习转移学习在XAI,XAI就是XAI.

更多相关视频

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

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

Published on: August 16, 2020

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

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

Published on: October 13, 2023

相关实验视频

Last Updated: Jun 27, 2025

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

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

Published on: October 10, 2018

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

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

Published on: August 16, 2020

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

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

Published on: October 13, 2023

科学领域:

在瘤学瘤学.
医疗成像医学成像
人工智能的人工智能

背景情况:

癌症,特别是肺癌和结肠癌,对全球健康构成重大威胁,需要准确及时诊断.
组织病理学分析对于有效的癌症治疗计划至关重要.
早期发现癌症可以显著降低死亡率.

研究的目的:

开发和验证一个机器学习模型,以提高从组织病理图像诊断肺癌和结肠癌的准确性.
将深度学习与基于纹理的功能集成在一起,以提高诊断性能.
用可解释的AI (XAI) 技术来实现模型透明度.

主要方法:

实现Inception-ResNetV2深度学习架构的实施.
整合本地二进制模式 (LBP) 的纹理特征.
培训和评估对一个数据集的组织病理图像.
应用夏普利添加式解释 (SHAP) 来实现模型的解释性.

主要成果:

拟议的模型实现了99.98%的异常诊断准确率.
深度学习和LBP的结合显著改善了癌症的识别.
SHAP分析为模型的决策过程提供了洞察力,增强了信任和透明度.

结论:

具有LBP特征的Inception-ResNetV2模型在自动化癌症诊断中表现出高效率.
可解释的人工智能增强了深度学习模型在瘤学中的临床适用性.
这种方法有可能彻底改变癌症诊断,导致更准确,更可靠的医学评估.