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

Gross Anatomy of the Lungs01:17

Gross Anatomy of the Lungs

The lungs are a pair of vital organs connected to the trachea via the left and right bronchi. The base of these organs meets the dome-shaped muscle known as the diaphragm. Encased by the pleurae, the lungs contact the mediastinum. The right lung is shorter yet wider, and has a larger volume than the left lung. The left lung has an indentation known as the cardiac notch. The superior region of the lungs is referred to as the apex, whereas the base is the lower region near the diaphragm. The...

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

Noncontact Cardiac Activity Detection Based on Single-Channel ISM Band FMCW Radar.

Biosensors·2023
Same author

Improved Complementary Pulmonary Nodule Segmentation Model Based on Multi-Feature Fusion.

Entropy (Basel, Switzerland)·2022
查看所有相关文章

相关实验视频

Updated: Jun 27, 2026

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
10:26

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

1.8K

肺结节细分和分类的多任务模型

Tiequn Tang1, Rongfu Zhang2,3

  • 1School of Physics and Electronic Engineering, Fuyang Normal University, Fuyang 236037, China.

Journal of imaging
|September 27, 2024
PubMed
概括

这项研究介绍了MT-Net,这是一种用于肺癌诊断的新型深度学习模型. 它同时对肺结节进行细分和分类,通过利用任务相关性来实现这两项任务的高准确性.

关键词:
肺结节的分类 肺结节的分类肺结节细分 细分 肺结节细分多任务网络网络多任务网络预测蒸蒸的时间任务相关性 任务相关性

更多相关视频

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.4K
Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy
05:24

Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy

Published on: January 10, 2025

330

相关实验视频

Last Updated: Jun 27, 2026

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
10:26

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

1.8K
Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.4K
Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy
05:24

Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy

Published on: January 10, 2025

330

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算机辅助诊断 计算机辅助诊断

背景情况:

  • 肺结节的准确细分和分类对于肺癌诊断至关重要.
  • 现有的深度学习模型经常将细分和分类作为单独的任务来处理,忽视了任务相关性带来的潜在性能增长.

研究的目的:

  • 开发一个统一的多任务网络 (MT-Net) 以同时进行肺结节细分和分类.
  • 利用任务相关性来提高细分和分类的性能.

主要方法:

  • 提出了一个多任务网络 (MT-Net),具有共享的骨干和预测蒸结构.
  • MT-Net由粗细分,分类和细细分的子网络组成.
  • 使用LIDC-IDRI数据集进行定量和定性分析.

主要成果:

  • 在肺结节细分方面获得了83.2%的Dice相似系数 (DI).
  • 在良性和恶性肺结节分类方面获得了91.9%的准确性 (ACC).
  • 与最先进的方法相比,已证明具有竞争力的性能.

结论:

  • 一个统一的多任务模型可以有效地提高肺结节细分和分类性能.
  • 利用细分和分类任务之间的相关性可以提高肺癌检测的诊断准确性.