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

Ultrasonography01:17

Ultrasonography

4.4K
Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called...
4.4K

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

AI-based selection of tumor regions for genomic profiling in neuropathology.

Neuro-oncology advances·2026
Same author

Clinical decision support in hematological malignancies using a case-grounded AI agent.

Nature medicine·2026
Same author

A deep learning framework for efficient pathology image analysis.

Nature communications·2026
Same author

Functional Outcome Prediction in Young Adults With Mental Health Symptoms Using Machine Learning and Large Language Models: Longitudinal Observational Study.

JMIR mental health·2026
Same author

Counterfactual Diffusion Models Provide Interpretable Explanations of Artificial Intelligence Models in Pathology.

Cancer research·2026
Same author

Towards autonomous medical artificial intelligence agents.

Nature·2026

相关实验视频

Updated: Jun 19, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

2.7K

在放射学中弱监督的深度学习.

Leo Misera1, Gustav Müller-Franzes1, Daniel Truhn1

  • 1From the Institute and Polyclinic for Diagnostic and Interventional Radiology (L.M.), Else Kröner Fresenius Center for Digital Health (L.M., J.N.K.), and Department of Medicine I (J.N.K.), Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstrasse 74, 01307 Dresden, Germany; Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany (G.M.F., D.T.); and Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany (J.N.K.).

Radiology
|July 23, 2024
PubMed
概括
此摘要是机器生成的。

弱监督学习提供了一个可扩展的方法,通过使用不完美的标签来训练放射学中的深度学习 (DL) 模型. 这种方法解锁了大型数据集,用于在医学成像分析中推进人工智能.

更多相关视频

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.8K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

384

相关实验视频

Last Updated: Jun 19, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

2.7K
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.8K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

384

科学领域:

  • 放射学 放射学是一门学科.
  • 人工智能的人工智能
  • 医疗成像医学成像

背景情况:

  • 深度学习 (DL) 是放射性图像分析中的AI标准.
  • 传统的DL模型需要广泛的手动专家标签,限制了可扩展性.
  • 弱监督学习 (WSL) 提供了一个更具可扩展性的替代方案.

研究的目的:

  • 在放射学中概述WSL的关键概念.
  • 在放射图像分析中提供WSL应用的概述.
  • 突出WSL在促进DL采用和生物标志物开发方面的潜力.

主要方法:

  • 探索WSL原则,包括不完整,不准确和不准确的监督.
  • 讨论使用大型语言模型从放射学报告中自动提取弱标签.
  • 对WSL在放射学分析中的当前和潜在应用进行审查.

主要成果:

  • WSL可以利用大型,不完美标记的数据集进行DL模型训练.
  • 从自由文本报告中自动提取弱标签是一种可行的方法.
  • WSL可以克服传统监管方法固有的数据限制.

结论:

  • WSL对于释放放射学大数据集的潜力至关重要.
  • 进一步的研究可以加速将WSL整合到临床和研究工作流程中.
  • WSL可以推动基于DL的新生物标志物的开发,并加强放射学中人工智能的采用.