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

Clinical applications of digital twin technology in In Vitro Fertilisation.

Journal of gynecology obstetrics and human reproduction·2026

Same author

Translational Gaps in Immuno-AI: From algorithmic accuracy to clinical trust.

Human immunology·2026

Same author

Mitigating algorithmic bias in AI-powered toxicology: Frameworks for explainable and equitable predictions in human health and environmental safety.

Toxicology letters·2026

Same author

Digital twins in urological oncology and surgery: A review of emerging applications.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology·2026

Same author

Digital twin technology in forensic mental health.

Journal of forensic and legal medicine·2026

Same author

Digital twin applications in radiology and radiotherapy: Applications, challenges, and future perspectives.

European journal of radiology·2026

Search research articles

相关实验视频

Updated: Sep 17, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

通过使用深度转移学习在医学成像中增强白血病检测.

Afeez A Soladoye¹, David B Olawade², Ibrahim A Adeyanju¹

¹Department of Computer Engineering, Federal University, Oye-Ekiti, Nigeria.

International journal of medical informatics

|June 29, 2025

概括

像EfficientNet-B3这样的深度转移学习模型显示出早期急性淋巴细胞白血病 (ALL) 检测的前景. EfficientNet-B3实现了96%的准确性,显著优于VGG-19,提供了一个计算高效的诊断工具.

关键词:

急性淋巴细胞白血病 (Acute Lymphoblastic Leukemia) 是一种急性淋巴细胞白血病.癌症癌症癌症癌症深度转移学习是指深度转移学习.有效的Net-B3 有效的Net-B3 医学图像分类医学图像分类

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

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

相关实验视频

Last Updated: Sep 17, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

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

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

科学领域:

医疗成像医学成像
计算生物学计算生物学
在瘤学瘤学.

背景情况:

急性淋巴细胞白血病 (ALL) 是一种流行的儿科癌症.
早期发现ALL对于改善患者的治疗结果和降低治疗成本至关重要.
对ALL的传统诊断方法可能耗时且资源密集.

研究的目的:

评估深度转移学习算法的有效性,以早期检测急性淋巴细胞白血病 (ALL).
为了比较VGG-19和EfficientNet-B3模型在医学图像上的ALL分类中的性能.
为了确定一个计算效率高,准确的方法,用于ALL诊断.

主要方法:

利用了一个公共数据集,包括来自118名被诊断为ALL的患者的10,661张图像.
应用了两个转移学习算法:VGG-19和EfficientNet-B3.
通过调整大小,增强和规范化预处理数据,为100个时代培训模型.

主要成果:

EfficientNet-B3实现了96%的平均准确率,相比VGG-19的80% (p < 0.001) 的平均准确率要高得多.
EfficientNet-B3在处理阶级不平衡方面表现出卓越的表现,具有较高的精度,回忆和少数阶级的F1分数.
VGG-19的表现较差,特别是在少数群体中,这表明处理不平衡的数据集存在挑战.

结论:

EfficientNet-B3是一个高精度和计算效率的工具,用于早期检测ALL.
临床整合需要解决计算和整合方面的挑战.
未来的研究应该探索多模式数据集,以提高风险因素识别和诊断准确度.