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

Ensemble Model Based on Hybrid Deep Learning for Intrusion Detection in Smart Grid Networks.

Sensors (Basel, Switzerland)·2023
Same author

Deep Reinforcement Learning for Workload Prediction in Federated Cloud Environments.

Sensors (Basel, Switzerland)·2023
Same journal

Correction: Luca et al. Global and Regional Diagnostic Results of Progress Toward Cervical Cancer Elimination, According to the WHO Strategy: A Systematic Literature Review with Narrative Synthesis. <i>Diagnostics</i> 2026, <i>16</i>, 1224.

Diagnostics (Basel, Switzerland)·2026
Same journal

Association Between Systemic Inflammatory Response Biomarkers and Disease Activity in Systemic Lupus Erythematosus: A Multi-Center Retrospective Study.

Diagnostics (Basel, Switzerland)·2026
Same journal

Vertebrogenic Low Back Pain and Basivertebral Nerve Ablation: A Review of Mechanisms, Imaging-Driven Selection, and Clinical Outcomes.

Diagnostics (Basel, Switzerland)·2026
Same journal

Multivalvular Carcinoid Heart Disease: The Role of Echocardiography in Diagnosis and Selection for Heterotopic Bicaval Valve Implantation.

Diagnostics (Basel, Switzerland)·2026
Same journal

Data-Efficient and Explainable Multimodal Survival Prediction in NSCLC Using Deep Image Embeddings, Clinical Variables, and Gradient-Boosted Trees.

Diagnostics (Basel, Switzerland)·2026
Same journal

Anomalous Left Coronary Artery from the Pulmonary Artery: Cinematic Volume Rendering Technique for Enhanced Anatomic Visualization.

Diagnostics (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jan 18, 2026

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
10:37

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

1.1K

增强乳腺癌诊断使用多模式功能融合与放射学和转移学习.

Nazmul Ahasan Maruf1, Abdullah Basuhail1, Muhammad Umair Ramzan1

  • 1Faculty of Computing and Information Technology, Department of Computer Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Diagnostics (Basel, Switzerland)
|September 13, 2025
PubMed
概括
此摘要是机器生成的。

这项研究通过整合放射学和深度学习 (DL) 功能来增强乳腺癌检测,通过ResNet152.2.实现97%的准确性. 该方法提高了早期癌症识别的诊断精度和稳定性.

关键词:
乳腺癌 乳腺癌 乳腺癌深度学习是一种深度学习.功能工程的特点工程.医学成像医学成像放射学分析的分析方法转移学习转移学习

更多相关视频

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
06:08

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

1.2K
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

7.4K

相关实验视频

Last Updated: Jan 18, 2026

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
10:37

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

1.1K
A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
06:08

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

1.2K
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

7.4K

科学领域:

  • 医学成像和人工智能 医学成像和人工智能
  • 计算病理学计算病理学
  • 生物医学信息学 生物医学信息学

背景情况:

  • 乳腺癌是全球癌症死亡的主要原因,需要改进早期检测方法.
  • 目前的早期检测依赖于医学成像,人工智能 (AI),放射学和深度学习 (DL) 显示出希望.
  • 诸如有限的数据,过度拟合和糟糕的概括等挑战阻碍了AI模型在乳腺癌检测中的性能.

研究的目的:

  • 通过结合放射学和深度学习 (DL) 功能来提高乳腺癌检测的准确性和稳定性.
  • 用先进的数据增强技术克服数据限制和模型过拟合.
  • 开发一个统一的多式联运特征空间,以提高分类性能.

主要方法:

  • 从CBIS-DDSM数据集中提取了使用PyRadiomics的放射学特征和通过转移学习模型的深度学习特征.
  • 应用数据增强以减轻过度拟合和改善模型通用化.
  • 集成的放射学和深度功能,培训和评估13个预训练的转移学习模型,包括ResNet,DenseNet,InceptionV3,MobileNet和VGG.

主要成果:

  • ResNet152实现了最高的分类准确率97%,证明了提高诊断精度的巨大潜力.
  • 其他模型,如VGG19,ResNet101V2和ResNet101,也显示出高精度 (96%),突出了所选特征集的有效性.
  • 集成的多式联运功能空间有助于强大的乳腺癌检测.

结论:

  • 放射学和深度学习方法的结合显示出对准确和强大的乳腺癌检测有重大前景.
  • 未来的研究应该探索视觉转换器 (ViT) 架构和多模式数据集成 (临床,基因组) 以进一步改进.
  • 这种方法有可能彻底改变乳腺癌检测,使其更准确,更易于解释和更易于适应.