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

Integrating Participatory Social Innovation Into Requirements Engineering for AI Health Care Solutions: Case Study.

Journal of medical Internet research·2026
Same author

Strengthening cancer prevention in european schools: a randomized controlled trial of digital interventions in adolescents: the SUNRISE program.

BMC public health·2026
Same author

Big Data and Trustworthy AI for Heart Failure: A Review.

Circulation. Heart failure·2026
Same author

Artificial intelligence-driven radiomics: developing valuable radiomics signatures with the use of artificial intelligence.

BJR artificial intelligence·2026
Same author

Bridging gaps in youth mental health care: YOUTHreach-a comprehensive European strategy.

European child & adolescent psychiatry·2026
Same author

Baseline risk factors for the development of open-angle glaucoma in a 12-year glaucoma incidence study: the Thessaloniki Eye Study.

The British journal of ophthalmology·2026
Same journal

Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.

Journal of imaging·2026
Same journal

Coronary Artery Anomalies and Anatomical Variants: Cross-Sectional Diagnostic Imaging and Clinical Background.

Journal of imaging·2026
Same journal

YoLeTooth: A Unified Framework for Joint Tooth Segmentation and Periapical Lesion Detection in Panoramic Radiographs.

Journal of imaging·2026
Same journal

Radiomics-Guided Multi-Sequence Learning for Pathological Complete Response Prediction from Breast MRI with Missing Auxiliary Sequences.

Journal of imaging·2026
Same journal

Cutaneous Thermography in Arthropathies: Quantitative Imaging, Machine Learning, and Clinical Translation.

Journal of imaging·2026
Same journal

Two-Stage Dynamic Synergistic Segmentation Method for Myocardial Pathology.

Journal of imaging·2026
查看所有相关文章

相关实验视频

Updated: Jan 10, 2026

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

23.0K

可解释的基于放射学模型用于乳腺癌中自动图像质量评估 DCE MRI 数据数据

Georgios S Ioannidis1,2, Katerina Nikiforaki1, Aikaterini Dovrou1,3

  • 1Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology-Hellas (FORTH), 70013 Heraklion, Greece.

Journal of imaging
|November 26, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种可解释的放射学模型,自动评估乳腺癌动态对比增强磁共振成像 (DCE-MRI) 质量. 该模型准确地区分高质量的DCE-MRI扫描和低质量的DCE-MRI扫描,帮助临床实践.

关键词:
在DCEMRI中,DCEMRI是指DCE的MRI.乳房成像检查 乳房成像检查可以解释性的解释性.图像质量评估 图像质量评估机器学习是机器学习.客观的质量指标.无线电学 (radiomics) 是一种无线电学.

更多相关视频

Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model
06:24

Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model

Published on: April 18, 2015

15.6K
Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model
08:32

Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model

Published on: October 2, 2020

6.9K

相关实验视频

Last Updated: Jan 10, 2026

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

23.0K
Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model
06:24

Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model

Published on: April 18, 2015

15.6K
Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model
08:32

Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model

Published on: October 2, 2020

6.9K

科学领域:

  • 放射学和医学成像分析.
  • 医疗保健中的机器学习
  • 生物医学信号处理

背景情况:

  • 准确评估医学成像质量对于可靠的诊断至关重要.
  • 乳腺癌动态对比增强磁共振成像 (DCE-MRI) 质量可以显著影响诊断的准确性.
  • 现有的图像质量评估方法可能缺乏自动化和可解释性.

研究的目的:

  • 为乳腺癌DCE-MRI中自动图像质量评估开发一种可解释的基于放射学模型.
  • 评估机器学习分类器在区分高质量和低质量的DCE-MRI扫描中的性能.
  • 通过质量控制,提高大规模医学成像数据集的可靠性和公平性.

主要方法:

  • 从280张乳腺癌DCE-MRI图像中提取了819个放射性特征和2个无参考图像质量指标.
  • 从整个图像和感兴趣的背景区域提取特征,考虑两个场景:每个患者12个切片和中间切片.
  • 机器学习分类器 (包括支持矢量机器) 的应用,使用SHapley添加式解释 (SHAP) 评估可解释性.

主要成果:

  • 该模型在使用中间切片 (场景II) 的特征时实现了最佳性能,将整个图像和背景特征结合起来.
  • 一个支持矢量机器分类器产生了高性能指标:85.51%的灵敏度,80.01%的特异性,82.76%的准确性和89.37%的AUC.
  • SHAP分析为模型的预测提供了解释性,确定了影响质量评估的关键特征.

结论:

  • 开发的可解释的放射学模型显示了在乳腺癌DCE-MRI中自动图像质量评估的巨大潜力.
  • 该模型可以集成到临床工作流程中,以确保数据质量并提高诊断可靠性.
  • 这种方法为管理大型图像存储库和进行公平的子组分析提供了有价值的工具.