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

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

Novel Sulfonate Derivatives Functionalized with Triazole-Hydrazone Moieties: Synthesis, Characterization, DFT, Targeting Brain Tumors via DNA Damage, Cytotoxicity, Migration Suppression, Antimicrobial Activity, and In Silico Study.

Molecules (Basel, Switzerland)·2026
Same author

Study of benign prostatic hyperplasia and androgen pathway manipulation on lung cancer survivals.

Journal of advanced research·2026
Same author

Validity and reliability of the Turkish version of the general rehabilitation adherence scale in individuals with osteoarthritis.

BMC musculoskeletal disorders·2026
Same author

Plasma and aqueous humor levels of Maresin-1 in patients with diabetic retinopathy.

Scientific reports·2026
Same author

The structural and microbiological properties of human cadaveric iliac vessel grafts stored at a readily available standard freezer: a comprehensive analysis as a function of storage time.

Frontiers in surgery·2026
Same author

New bis-piperazine derivatives: synthesis, characterization (IR, NMR), gamma-ray absorption, antimicrobial activity, molecular docking and dynamics study.

Turkish journal of chemistry·2026

相关实验视频

Updated: Jun 11, 2025

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

2.3K

通过整体注意力机制增强脑瘤分类.

Fatih Celik1, Kemal Celik2, Ayse Celik3

  • 1Department of Geomatic Engineering, Yıldız Technical University, Esenler, Istanbul, Turkey. F.alpcelik@gmail.com.

Scientific reports
|September 27, 2024
PubMed
概括

这项研究引入了一个集体注意力机制,用于在MRI扫描中改进脑瘤检测. 这种新的方法显著提高了分类准确性,为医疗保健系统提供了强大的解决方案.

关键词:
注意力 注意力 注意力 注意力大脑瘤是什么?在美国,CNN是CNN.分类 分类 分类 分类.深度学习是一种深度学习.

更多相关视频

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

7.2K
Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping
13:12

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Published on: August 12, 2019

45.2K

相关实验视频

Last Updated: Jun 11, 2025

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

2.3K
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

7.2K
Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping
13:12

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Published on: August 12, 2019

45.2K

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算机视觉 计算机视觉

背景情况:

  • 大脑瘤是一个重大的全球健康问题,需要准确及时检测,以有效治疗患者并改善生活质量.
  • 磁共振成像 (MRI) 是脑成像的主要工具,但由于复杂的解剖变异,在MRI扫描中精确识别瘤是复杂的.

研究的目的:

  • 开发和评估一种创新的集体注意力机制,以提高MRI图像中脑瘤检测的准确性.
  • 通过利用多层次的特征提取和注意力机制来解决MRI中精确瘤识别的挑战.

主要方法:

  • 拟议的方法使用MobileNetV3和EfficientNetB7来提取中间和最终特征地图.
  • 在中间和最后的特征地图层面都集成了一个共同注意机制,然后进行组合以增强特征表示.
  • 这种方法侧重于通过将注意力集中在MRI数据中的关键区域来提取全球层面的特征.

主要成果:

  • 整体注意力机制在检测当地和全球层面的各种特征模式方面表现出卓越的表现.
  • 该系统实现了高准确率:Figshare数据集的准确率为98.94%,BraTS 2019数据集的准确率为98.48%.
  • 性能指标表明,拟议的方法超越了现有的脑瘤分类技术.

结论:

  • 开发的集体注意力机制是医学成像中大脑瘤检测的强大而有效的工具.
  • 这种创新方法显示了将其整合到医疗保健系统中的重大前景,以提高诊断准确性和患者的治疗结果.