Search research articles

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 Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

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

Adaptive Multiscale Spatiotemporal Mixing Network for Multiview Seizure Detection.

International journal of neural systems·2026

Same author

To dye or not to dye? Comparing real-world diagnostic performance of white light, electronic, and Lugol's chromoendoscopy for esophageal squamous neoplasia.

Surgical endoscopy·2026

Same author

Attention-based Multimodal Spatiotemporal Enhanced Interaction Network For Major Depressive Disorder Detection.

IEEE journal of biomedical and health informatics·2026

Same author

A Practical Distributed Virtual Bolus Strategy for Motion-Robust Superficial Dose Delivery in Breast Volumetric Modulated Arc Therapy.

Practical radiation oncology·2026

Same author

Accelerated brain aging in amyotrophic lateral sclerosis and its prognostic associations: a cohort study.

BMC medicine·2026

Same author

Contact networks encode the ATP-induced dynamic structural asymmetry of condensin head domains.

The Journal of chemical physics·2025

Search research articles

相关实验视频

Updated: Jul 23, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

基于3D医疗图像细分的层次解卷积的特征交互网络.

Longfeng Shen^1,2,3, Yingjie Zhang¹, Qiong Wang¹

¹Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior (ICACB), College of Computer Science and Technology, Huaibei Normal University, Huaibei, Anhui, China.

|July 13, 2023

概括

这项研究引入了一种改进的深度学习方法,用于在3D医学图像中对脑瘤进行细分. 这种新的方法提高了临床应用的准确性和效率,例如手术规划.

更多相关视频

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

相关实验视频

Last Updated: Jul 23, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

科学领域:

医学成像医学成像
人工智能的人工智能是人工智能.
神经科学是一个神经科学.

背景情况:

手动对多模式脑瘤进行细分是耗时且具有挑战性的.
准确的细分对于临床治疗决策和手术规划至关重要.
由于瘤多样性和有限的计算资源,深度学习在医学图像细分方面面临挑战.

研究的目的:

开发一种自动和准确的方法来细分多模式脑瘤.
为了提高神经网络细分的性能,使用一种新的功能融合模块和注意力机制.
为了解决医疗图像细分中的类别失衡问题.

主要方法:

提出了一个基于层次解卷积网络和注意力机制的特征融合模块.
用功能融合模块取代U形网络跳过连接.
引入了一个全球关注机制,以整合编码器功能并探索上下文信息.

主要成果:

在BraTS 2019数据集上实现了0.775 (增强瘤),0.900 (整个瘤) 和0.827 (瘤核心) 的子相似系数 (DSC).
在2018年BraTS数据集上实现了0.800 (增强瘤),0.902 (整个瘤) 和0.841 (瘤核心) 的DSC.
证明了该方法在脑瘤图像研究中的普遍性和有效性.

结论:

拟议的方法为脑瘤图像分析提供了一个强大的工具.
功能融合模块和注意力机制有效地提高了细分精度.
该方法为复杂的医疗图像细分任务提供了通用的解决方案.