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

相关实验视频

Updated: Mar 10, 2026

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

3.6K

深度学习用于大脑瘤分类.

Justin S Paul1, Andrew J Plassard1, Bennett A Landman1,2

  • 1Computer Science, Vanderbilt University, Nashville, TN, USA 37235.

Proceedings of SPIE--the International Society for Optical Engineering
|March 9, 2026
PubMed
概括
此摘要是机器生成的。

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

Quantifying brain development in the HEALthy Brain and Child Development (HBCD) Study: The magnetic resonance imaging and spectroscopy protocol.

Developmental cognitive neuroscience·2024
Same author

Cell Spatial Analysis in Crohn's Disease: Unveiling Local Cell Arrangement Pattern with Graph-based Signatures.

Proceedings of SPIE--the International Society for Optical Engineering·2024
Same author

Predicting Age from White Matter Diffusivity with Residual Learning.

Proceedings of SPIE--the International Society for Optical Engineering·2024
Same author

Evaluation of Mean Shift, ComBat, and CycleGAN for Harmonizing Brain Connectivity Matrices Across Sites.

Proceedings of SPIE--the International Society for Optical Engineering·2024
Same author

Learning-based Free-Water Correction using Single-shell Diffusion MRI.

Proceedings of SPIE--the International Society for Optical Engineering·2024
Same author

Nucleus subtype classification using inter-modality learning.

Proceedings of SPIE--the International Society for Optical Engineering·2024
Same journal

AVA: Automated Viewability Analysis for Ureteroscopic Intrarenal Surgery.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Kidney Endoscopy Video to Preoperative CT Alignment for Depth Estimation.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Deep learning‑based cell type prediction in lung tissue from brightfield histology using CODEX-derived labels.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Reconstructing physiological signals from fMRI across the adult lifespan.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Axially Swept Light-Sheet Microscopy using scattering and fluorescence contrast mechanisms.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Analytic Bounds on GAMLSS Model Variability of Normative White Matter Brain Charts.

Proceedings of SPIE--the International Society for Optical Engineering·2026
查看所有相关文章

深度学习使用MRI图像准确地分类大脑瘤. 这种方法获得了超过91%的准确性,超过了用于脑膜瘤,质瘤和垂体瘤检测的专用方法.

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 神经科学是一个神经科学.

背景情况:

  • 深度学习 (DL) 在监督机器学习和图像分类方面表现有前途.
  • 准确的脑瘤分类对于有效的治疗计划至关重要.

研究的目的:

  • 应用深度学习方法来分类脑膜瘤,质瘤和脑垂体瘤的脑图像.
  • 评估完全连接和卷积神经网络在这个任务上的表现.

主要方法:

  • 利用了来自191名患者的989张轴向T1加权对比增强MRI (CE-MRI) 图像的数据集.
  • 经过训练和测试的完全连接和卷积神经网络,包括数据增强.
  • 在绩效评估中采用五重交叉验证策略.

主要成果:

  • 最好的训练神经网络实现了91.43%的平均交叉验证准确率.
  • 深度学习模型在分类不同类型的大脑瘤方面表现出高准确度.
  • 一般的深度学习方法在瘤分类方面表现优于专业方法.

结论:

  • 深度学习是使用MRI数据进行脑瘤分类的高效方法.
关键词:
脑瘤分类大脑瘤的分类深度学习是一种深度学习.质瘤 质瘤 是一种机器学习是机器学习.脑膜瘤是指脑膜的一个部分.神经网络的神经网络的神经网络pituitary pituitary pituitary pituitary pituitary pituitary pituitary pituitary pituitary pituitary 下垂体 下垂体 下垂体 下垂体 下垂体 下垂体 下垂体 下垂体监督的分类监督的分类.

相关实验视频

Last Updated: Mar 10, 2026

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

3.6K
  • 这项研究强调了DL的潜力,以提高诊断准确性和潜在的患者结果.
  • 这些发现表明DL可以成为神经瘤学中一个有价值的工具,用于自动瘤检测和分类.