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

Cancer Survival Analysis01:21

Cancer Survival Analysis

357
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
357

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

Mapping radiosensitivity in glioblastoma using MR elastography and biomechanical modeling.

Scientific reports·2026
Same author

Automated delineation of putative non-contrast-enhancing tumor in glioblastoma: Prognostic insights.

Neuro-oncology·2026
Same author

Biomechanical mapping of tumor growth: A novel method to quantify glioma infiltration and mass effect.

Medical physics·2026
Same author

Comparing Glymphatic Function Measures: Diffusion Tensor Image Analysis Along Perivascular Spaces (DTI-ALPS) versus Intrathecal Contrast-Enhanced MRI.

Radiology·2026
Same author

Modeling glioma-induced impairments on the glymphatic system.

Fluids and barriers of the CNS·2026
Same author

Monitoring the lateral ventricles in the presence of intracranial hemorrhage using automated dual segmentation.

Medical physics·2026

相关实验视频

Updated: Jul 13, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

通过使用可视化和文本解释来解释质瘤存活率分类的深度学习模型.

Michael Osadebey1, Qinghui Liu2, Elies Fuster-Garcia3

  • 1Department of Physics and Computational Radiology, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Sognsvannsveien 20, 0372, Oslo, Norway. osademic@gmail.com.

BMC medical informatics and decision making
|October 18, 2023
PubMed
概括

这项研究通过从突出性地图中提取领域知识,提高了对质瘤存活率预测的深度学习解释性. 关键大脑区域附近的瘤表明生存时间较短,有助于临床决策.

关键词:
三维梯度加权类激活映射 (3D-Grad-CAM)卷积神经网络 (CNN) 是一种神经网络.深度学习是一种深度学习.质母细胞瘤 (glioblastoma) 是一个磁共振成像 (MR1) 是一种磁共振成像.

更多相关视频

Quantitative Immunohistochemistry of the Cellular Microenvironment in Patient Glioblastoma Resections
05:45

Quantitative Immunohistochemistry of the Cellular Microenvironment in Patient Glioblastoma Resections

Published on: July 31, 2017

9.7K
Implementation of Minimally Invasive Brain Tumor Resection in Rodents for High Viability Tissue Collection
08:23

Implementation of Minimally Invasive Brain Tumor Resection in Rodents for High Viability Tissue Collection

Published on: May 9, 2022

4.4K

相关实验视频

Last Updated: Jul 13, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
Quantitative Immunohistochemistry of the Cellular Microenvironment in Patient Glioblastoma Resections
05:45

Quantitative Immunohistochemistry of the Cellular Microenvironment in Patient Glioblastoma Resections

Published on: July 31, 2017

9.7K
Implementation of Minimally Invasive Brain Tumor Resection in Rodents for High Viability Tissue Collection
08:23

Implementation of Minimally Invasive Brain Tumor Resection in Rodents for High Viability Tissue Collection

Published on: May 9, 2022

4.4K

科学领域:

  • 神经成像是一种神经成像.
  • 人工智能的人工智能
  • 在瘤学瘤学.

背景情况:

  • 基于 Saliency 的算法有助于理解来自输入图像的深度学习模型的预测.
  • 从原始突出性图表中评估图像特征和预测的临床意义可能具有挑战性.
  • 这项研究旨在提高深度学习模型的可解释性,以对质瘤患者的生存率进行分类.

研究的目的:

  • 为了提高深度学习模型的可解释性,用于质瘤存活率分类.
  • 从突出性地图中提取基于领域知识的信息,以提高临床价值.
  • 为了将瘤位置和体积与患者生存结果相关联.

主要方法:

  • 利用了来自BraTs 2020挑战的147名质瘤患者手术前MRI扫描 (T1,T2,T2-FLAIR) 的数据集.
  • 开发了一个3D卷积神经网络 (CNN) 用于将患者分为短期,中期和长期生存组.
  • 将2D梯度加权类激活映射 (Grad-CAM) 扩展到3D,并与SRI 24解剖图谱集成,以提取特定区域的信息.

主要成果:

  • 较大的瘤体积与较短的整体存活率 (OS) 相相关.
  • 确定了与不同生存时间 (短期,中期,长期) 相关的特定瘤位置.
  • 横 temporal gyrus, fusiform 和 palladium 的瘤分别与短期,中期和长期生存有关.
  • 突出瘤位置和区域大脑对OS预测的贡献,帮助医生分析.

结论:

  • 从突出性地图中提取领域知识显著提高了深度学习模型的可解释性.
  • 位于雄辩的大脑区域的瘤与较差的患者生存结果有关.
  • 开发的方法为了解质瘤预后和支持临床决策提供了宝贵的见解.