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

Computed Tomography01:10

Computed Tomography

4.3K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
4.3K

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

Associations of hemoglobin levels with structural knee MRI findings at 33 years of age in a general population-based birth cohort.

Osteoarthritis and cartilage open·2026
Same author

Associations of health parameters at 16 years of age with structural knee MRI findings at 33 years of age in a general population-based birth cohort.

Osteoarthritis and cartilage open·2026
Same author

Clinical Feasibility of Deep Learning Contrast Synthesis From MR Fingerprinting in Knee Osteoarthritis.

Journal of magnetic resonance imaging : JMRI·2026
Same author

Associations of clinical measures and structural knee magnetic resonance imaging findings with knee symptoms in a birth cohort of 33-year-old adults.

Osteoarthritis and cartilage open·2026
Same author

Usability of Amorphous Manganese Oxide for Assessing the Proteoglycan Content in Articular Cartilage.

Magnetic resonance in chemistry : MRC·2025
Same author

The Evolution of Medical Student Competencies and Attitudes in Digital Health Between 2016 and 2022: Comparative Cross-Sectional Study.

JMIR medical education·2025

相关实验视频

Updated: Jun 9, 2025

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

22.4K

从数字乳腺图解合成数据进行成像表型评估:一项初步研究

Antti Isosalo1, Satu I Inkinen2, Lucia Prostredná3

  • 1Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland.

Computers in biology and medicine
|October 25, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种深度学习模型来分析数字乳腺图片合成 (DBT) 图像,改善乳腺组织模式的特征,以更准确地检测癌症. 人工智能通过分类具有高回忆度和特异性的组织类型来增强诊断评估.

关键词:
医学图像计算医学图像计算代表性的学习学习.断层成像扫描图像成像技术

更多相关视频

Terahertz Imaging and Characterization Protocol for Freshly Excised Breast Cancer Tumors
08:56

Terahertz Imaging and Characterization Protocol for Freshly Excised Breast Cancer Tumors

Published on: April 5, 2020

10.8K
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

42.8K

相关实验视频

Last Updated: Jun 9, 2025

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

22.4K
Terahertz Imaging and Characterization Protocol for Freshly Excised Breast Cancer Tumors
08:56

Terahertz Imaging and Characterization Protocol for Freshly Excised Breast Cancer Tumors

Published on: April 5, 2020

10.8K
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

42.8K

科学领域:

  • 放射学和医学成像学 医学成像学
  • 医疗保健中的人工智能
  • 在瘤学瘤学.

背景情况:

  • 数字乳腺瘤合成 (DBT) 是诊断乳腺癌的关键成像工具.
  • 目前的方法需要进一步精细化,以进行详细的组织模式表征.
  • 深度学习为乳房镜中的高级图像分析提供了潜力.

研究的目的:

  • 开发和评估一种基于深度学习的方法,用于在DBT数据中表征乳腺组织模式.
  • 将组织样本分为恶性,良性和正常类别.
  • 为了进行更复杂的特定组织异常的分类,如质量和建筑扭曲.

主要方法:

  • 来自DBT研究的5388个2D图像补丁的数据集被策划.
  • 一个补丁分类器被训练为两个分类场景:恶性-良性-正常和详细的异常分类.
  • 使用转移学习,从预先训练的全球意识多个实例分类器初始化模型权重.

主要成果:

  • 在恶性,良性和正常组织分类方面实现了高回忆和特异性.
  • 详细的质量分类和建筑扭曲也显示出有希望的回忆和特异性值.
  • 在良性建筑扭曲和良性质量分类之间观察到一些混.

结论:

  • 开发的深度学习表型分类器提高了DBT图像的评估.
  • 将这种分类器与标准恶性-良性-正常分类相结合,可以提供更详细的诊断信息.
  • 这种方法有可能提高使用DBT诊断乳腺癌的准确性和细粒度.