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

Pneumothorax-II01:27

Pneumothorax-II

104
Pneumothorax is a medical condition defined by the buildup of air in the pleural space between the lungs and the chest wall. This accumulation of air can lead to partial or complete lung collapse, resulting in a range of clinical manifestations. Understanding the clinical presentation and effective management strategies is crucial for healthcare professionals in providing timely and appropriate care to individuals with pneumothorax.
Clinical Manifestations:
104
Pneumothorax-I01:26

Pneumothorax-I

154
A pneumothorax is a condition where air builds up in the space between the lung and the chest wall, causing the lung to collapse. This condition arises when air enters the space between the parietal and visceral pleura, disrupting the negative pressure essential for lung inflation. This can lead to a partial or complete collapse of the lung.
Pneumothorax can be even further classified as spontaneous, traumatic, and tension pneumothorax.
154
Pleura of the Lungs01:13

Pleura of the Lungs

1.3K
The lungs are nestled in a cavity, shielded by the pleura. The pleura, a form of serous membrane, wraps around each lung. This membrane arrangement consists of two layers: the visceral and parietal pleurae. The visceral pleura lines the surface of the lungIn contrast, the parietal pleura is the outer layer and contacts to the thoracic wall, the mediastinum, and the diaphragm. The hilum is the point of connection between the visceral and parietal layers. The space between the parietal and...
1.3K

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

Automatic segmentation of nasopharyngeal carcinoma on CT images using efficient UNet-2.5D ensemble with semi-supervised pretext task pretraining.

Frontiers in oncology·2022
Same author

A3C-GS: Adaptive Moment Gradient Sharing With Locks for Asynchronous Actor-Critic Agents.

IEEE transactions on neural networks and learning systems·2020
Same author

Using deep-belief networks to understand propensity for livelihood change in a rural coastal community to further conservation.

Conservation biology : the journal of the Society for Conservation Biology·2020
Same author

ESP: an expert system for poisoning diagnosis and management.

Informatics for health & social care·2010
Same author

Parameter estimation using Simulated Annealing for S-system models of biochemical networks.

Bioinformatics (Oxford, England)·2006
Same journal

Prediction of germline BRCA mutation using clinicopathologic, MRI semantic, and radiomics features in high-risk breast cancer patients: a multicenter study.

Frontiers in radiology·2026
Same journal

The efficacy of multisite MRI scanners for total brain volume measurements: a cross-sectional study in Saudi Arabia.

Frontiers in radiology·2026
Same journal

Deep learning image reconstruction technique for improving image quality and radiation dose reduction compared to iterative reconstruction technique in non-contrast CT head imaging.

Frontiers in radiology·2026
Same journal

Self-adaptive forward-forward network for anomaly detection and medical image analysis.

Frontiers in radiology·2026
Same journal

Case Report: Structured MRI assessment of posterior thalamic infarction in a distribution compatible with posterior choroidal artery territory presenting as Déjerine-Roussy syndrome in an adolescent: differentiating arterial ischemia from venous thrombosis and thalamic neoplasm.

Frontiers in radiology·2026
Same journal

Bringing light to the reading room.

Frontiers in radiology·2026
查看所有相关文章

相关实验视频

Updated: May 8, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

14.0K

从胸部X射线放射图中检测和细分肺胸部,使用基于补丁的全卷积编码器解码器网络.

Jakov Ivan S Dumbrique1,2, Reynan B Hernandez3,4, Juan Miguel L Cruz4

  • 1Computer Vision and Machine Intelligence Group, Department of Computer Science, University of the Philippines-Diliman, Quezon City, Philippines.

Frontiers in radiology
|December 26, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的深度学习模型,用于在胸部X射线中准确检测和细分肺胸部X射线. 这种基于人工智能的方法提高了诊断效率,有可能改善重症监护机构患者的治疗结果.

关键词:
视觉变压器 视觉变压器自动图像细分自动图像细分胸部X射线 胸部X射线卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.诊断放射学 诊断放射学肺病理检测 肺病理检测肺部胸部 (pneumothorax) 是一个疾病.

更多相关视频

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

378
Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

496

相关实验视频

Last Updated: May 8, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

14.0K
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

378
Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

496

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
  • 计算机视觉 计算机视觉

背景情况:

  • 肺胸腔,或肺腔中的空气,是一种需要及时诊断的危急状况.
  • 胸部X射线是标准的,但微妙的肺胸部迹象会带来诊断挑战.
  • 自动检测系统可以帮助放射科医生识别这种危及生命的疾病.

研究的目的:

  • 开发和评估一个深度学习模型,用于在胸部X射线放射图中自动检测和细分肺胸部.
  • 用先进的AI技术提高肺胸部诊断的准确性和效率.
  • 为了解决微妙的膜线位移的手动解释的局限性.

主要方法:

  • 提出了一个新的深度学习架构,将完全卷积神经网络 (FCNNs) 和仅使用卷积模块的视觉转换器 (ViTs) 结合起来.
  • 该架构采用基于补丁的编码器-解码器结构,具有跳过连接以实现有效的功能集成.
  • 该模型在SIIM-ACR Pneumothorax Segmentation数据集和来自The Medical City的新编辑的数据集上进行了训练和验证.
  • 使用混合的Tversky和Focal损失函数来优化模型性能.

主要成果:

  • 与基线FCNN和先前的研究相比,拟议的模型在肺胸部检测和细分方面都显示出明显更高的准确性.
  • 该架构在保持计算效率的同时实现了高性能.
  • 废除研究证实,混合的Tversky和Focal损失函数比单独的Tversky损失提高了模型性能.

结论:

  • 开发的深度学习模型显示了提高肺胸检测诊断准确性和效率的巨大潜力.
  • 人工智能驱动的方法可以成为帮助放射科医生在临床环境中的宝贵工具.
  • 这项研究有助于推进自动化医疗图像分析,用于关键条件,如肺胸部.