Jove
Visualize
联系我们

相关概念视频

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

Bioinformatics Approaches for Functional and Structural Annotation and Molecular Docking Study of a Hypothetical Protein From Staphylococcus aureus.

BioMed research international·2026
Same author

Rhamnogalacturonan-II Dimerisation Reinforces Salt Resistance in Sugar Beet.

Plant, cell & environment·2026
Same author

High-resolution gridded streamflow data for Ganges-Brahmaputra-Meghna River Basins in Bangladesh (1951-2023).

Scientific data·2025
Same author

ContourTL-Net: Contour-Based Transfer Learning Algorithm for Early-Stage Brain Tumor Detection.

International journal of biomedical imaging·2024
Same author

Analysis and identification of genomic and immunogenic features of dengue serotype 3 genomes obtained during the 2019 outbreak in Bangladesh.

New microbes and new infections·2022
Same author

A review on machine learning and deep learning for various antenna design applications.

Heliyon·2022
Same journal

Deep Learning for Brain Tumour Analysis: A Systematic Review of CNN-Transformer Hybrids in Multimodal Imaging.

International journal of biomedical imaging·2026
Same journal

Brain Tumor Segmentation Using U-Net With ResNet50 Encoder for Enhanced MRI Analysis.

International journal of biomedical imaging·2026
Same journal

Generative AI-Driven CNN Framework for Enhanced Lung Cancer Detection, Prediction, and Treatment: A Novel Approach to Overcoming AI Limitations.

International journal of biomedical imaging·2026
Same journal

Enhancing the Generalizability of Deep Learning-Based Models for Lung Field Segmentation in Chest Radiographs Using Edge-Assisted Multiscale Feature Fusion.

International journal of biomedical imaging·2026
Same journal

Personalized PET Imaging in Gastric Cancer: An Umbrella Review of Meta-Analyses to Guide Radiopharmaceutical Selection and Clinical Indication.

International journal of biomedical imaging·2026
Same journal

Clinician-Centric Explainable Artificial Intelligence Framework for Medical Imaging Diagnostics: A Systematic Review.

International journal of biomedical imaging·2026
查看所有相关文章
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

相关实验视频

Updated: Sep 11, 2025

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.3K

增强脑瘤细分使用CBAM集成的深度学习和区域量化.

Rafiqul Islam1, Sazzad Hossain1

  • 1Department of Computer Science and Engineering, Dhaka University of Engineering & Technology, Gazipur, Bangladesh.

International journal of biomedical imaging
|August 11, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种改进的深度学习模型,用于MRI扫描中的脑瘤细分. 增强的U-Net架构与卷积块注意模块 (CBAM) 实现了更高的准确性和效率在划分瘤和测量其范围.

关键词:
量化面积的量化面积是什么意思脑瘤分析 脑瘤分析轻量级的U-Net模型磁共振成像技术的使用细分化 细分化的细分化

更多相关视频

Live Imaging of Microtubule Dynamics in Glioblastoma Cells Invading the Zebrafish Brain
09:29

Live Imaging of Microtubule Dynamics in Glioblastoma Cells Invading the Zebrafish Brain

Published on: July 29, 2022

2.8K
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

2.9K

相关实验视频

Last Updated: Sep 11, 2025

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.3K
Live Imaging of Microtubule Dynamics in Glioblastoma Cells Invading the Zebrafish Brain
09:29

Live Imaging of Microtubule Dynamics in Glioblastoma Cells Invading the Zebrafish Brain

Published on: July 29, 2022

2.8K
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

2.9K

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:

  • 从MRI精确的脑瘤细分对于诊断和治疗至关重要,但由于瘤的复杂性和手动细分的局限性而具有挑战性.
  • 需要自动化方法来提高效率并减少人类错误在划定脑瘤边界和量化瘤负担.

研究的目的:

  • 通过使用一种新的深度学习方法,提高基于MRI的脑瘤细分的准确性和效率.
  • 开发一个全面的框架,用于精确的瘤细分和定量瘤面积测量.

主要方法:

  • 将卷积块注意模块 (CBAM) 集成到基于VGG19的U-Net架构中.
  • 利用深度和点向卷曲来提高特征提取和处理效率.
  • 开发一种新的方法来计算基于细分像素的瘤面积,用于定量分析.

主要成果:

  • 拟议的深度学习框架在脑瘤细分方面表现出更高的精度.
  • 该模型有效地结合了瘤区域测量,为临床解释提供了可量化的数据.
  • 定性评估证实了该模型在细分瘤口罩方面的准确性和可靠性.

结论:

  • 开发的方法为大脑瘤分析提供了更好的细分精度,效率和临床相关性.
  • 这种方法有可能提高早期瘤诊断,治疗计划和患者监测.
  • 集成CBAM和高级卷积代表了自动神经成像分析的重大进步.