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

Neural Circuits01:25

Neural Circuits

1.3K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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

Correction: E2SVM: Electricity-Efficient SLA-aware Virtual Machine Consolidation approach in cloud data centers.

PloS one·2026
Same author

Enhancing tumor deepfake detection in MRI scans using adversarial feature fusion ensembles.

Scientific reports·2025
Same author

MultiFAR: Multidimensional information fusion with attention-driven representation learning for student performance prediction.

PloS one·2025
Same author

An MRI based histogram oriented gradient and deep learning approach for accurate classification of mild cognitive impairment and Alzheimer's disease.

Frontiers in medicine·2025
Same author

Modelling of queuing systems using blockchain based on Markov process for smart healthcare systems.

Scientific reports·2025
Same author

E2SVM: Electricity-Efficient SLA-aware Virtual Machine Consolidation approach in cloud data centers.

PloS one·2024

相关实验视频

Updated: Jul 18, 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

2.8K

桥接-U-Net-ASPP-EVO和深度学习优化用于大脑瘤细分.

Rammah Yousef1, Shakir Khan2,3, Gaurav Gupta1

  • 1Yogananda School of AI, Computers and Data Sciences, Shoolini University, Solan 173229, India.

Diagnostics (Basel, Switzerland)
|August 26, 2023
PubMed
概括

这项研究使用深度学习增强了MRI中的脑瘤细分,引入了一种新的桥梁U-Net-ASPP-EVO模型. 新架构显著提高了各种瘤子区域的细分精度.

关键词:
BraTS 20202021 年数据集桥接的U-Net是一个桥梁.大脑瘤的细分 脑瘤的细分空间金字塔的聚合方式.

更多相关视频

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

441
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.5K

相关实验视频

Last Updated: Jul 18, 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

2.8K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

441
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.5K

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 神经瘤学神经瘤学

背景情况:

  • 在MRI中手动细分脑瘤对于放射科医生来说是复杂和耗时的.
  • 准确的细分对于诊断,治疗计划和脑瘤监测至关重要.
  • 深度学习为自动化和提高脑瘤细分的准确性提供了潜力.

研究的目的:

  • 研究深度学习优化器和损失函数对脑瘤细分的影响.
  • 引入和评估一种新的深度学习架构,即 Bridged U-Net-ASPP-EVO,用于增强脑瘤细分.
  • 将拟议模型的性能与基准数据集上的最先进方法进行比较.

主要方法:

  • 对深度学习优化器和损失函数用于脑瘤细分的实验性评估.
  • 桥梁U-Net-ASPP-EVO架构的开发,其中包括Atrous空间金字塔聚合,演化规范化,挤压和激发块以及最大平均聚合.
  • 在MICCAI BraTS 2020和RSNA-ASNR-MICCAI BraTS 2021数据集上验证拟议架构的两个变体 (v1和v2).

主要成果:

  • 与现有最先进的模型相比,桥梁U-Net-ASPP-EVO模型取得了具有竞争力的结果.
  • 在ET,TC和WT分区的BraTS 2021验证数据集上,实现了0.84,0.85,0.91的1.0和0.83,0.86,0.92的2.0的变体.
  • 证明了整合多层次信息处理和高级规范化技术的有效性,以改善细分.

结论:

  • 拟议的桥梁U-Net-ASPP-EVO架构有效地从MRI扫描中对大脑瘤进行细分.
  • 这项研究强调了像Atrous空间金字塔聚合这样的建筑组件对于处理不同大小的瘤的重要性.
  • 开发的模型显示出在自动化脑瘤细分中临床应用的重大前景.