Jove
Visualize
联系我们

相关概念视频

Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
Cerebrospinal Fluid01:21

Cerebrospinal Fluid

Cerebrospinal fluid (CSF) is a colorless liquid that flows around the brain and the spinal cord, playing a vital role in the protection, support, and overall function of the central nervous system (CNS). CSF production, circulation, and absorption are tightly regulated processes essential for the brain and spinal cord to function properly.
CSF Production
CSF is produced mainly in the choroid plexus, a network of capillaries and ependymal cells located within the ventricular system of the brain.

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

Deep Learning-Based Evaluation of Maxillary Dental Midline Deviation on Orthodontic Frontal Photographs.

Bioengineering (Basel, Switzerland)·2026
Same author

An Explainable Web-Based Diagnostic System for Alzheimer's Disease Using XRAI and Deep Learning on Brain MRI.

Diagnostics (Basel, Switzerland)·2025
Same author

SeruNet-MS: A Two-Stage Interpretable Framework for Multiple Sclerosis Risk Prediction with SHAP-Based Explainability.

Neurology international·2025
Same author

Enhancing Melanoma Diagnosis with Advanced Deep Learning Models Focusing on Vision Transformer, Swin Transformer, and ConvNeXt.

Dermatopathology (Basel, Switzerland)·2024
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: May 12, 2026

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

40.3K

一个网络部署,可解释的人工智能系统用于全面的脑瘤诊断

Serra Aksoy1, Pinar Demircioglu2, Ismail Bogrekci2

  • 1Institute of Computer Science, Ludwig Maximilian University of Munich (LMU), Oettingenstrasse 67, 80538 Munich, Germany.

Neurology international
|August 27, 2025
PubMed
概括

这项研究开发了一种基于网络的深度学习系统,用于大脑瘤诊断,在分类和检测方面实现了高精度. 该平台提供可解释的细分和分类,增强放射性工作流程.

科学领域:

  • 神经瘤学
  • 医学成像
  • 人工智能

背景情况:

  • 准确的脑瘤诊断对于神经瘤治疗计划至关重要.
  • 用于二维分类和三维细分的深度学习模型可以增强放射性工作流程.
  • 可解释的人工智能 (XAI) 技术提高了这些模型的解释性.

研究的目的:

  • 开发一个基于网络的脑瘤细分和分类诊断平台.
  • 通过深度学习整合二维分类和三维细分.
  • 整合可解释的人工智能,以提高模型的可解释性.

主要方法:

  • 开发了一种结合2D瘤分类 (MobileNetV2) 和3D体积细分 (SegResNet) 的诊断系统.
  • 用于二进制瘤检测的元分类器MLP.
  • 使用XRAI地图和高斯叠加,集成到Web界面提供了可解释性.

主要成果:

  • 2D MobileNetV2模型实现了98. 09%的瘤分类准确度.
  • 3D SegResNet 模型获得了 68- 70% 的瘤细分率.
  • 该MLP瘤检测模块实现了100%的准确性,可解释性模块与病理特征一致.
关键词:
大脑瘤的诊断深度学习可解释的AI (XAI)容量细分基于网络的平台

更多相关视频

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging
06:44

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

Published on: June 7, 2020

7.5K
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

227

相关实验视频

Last Updated: May 12, 2026

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

40.3K
Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging
06:44

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

Published on: June 7, 2020

7.5K
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

227

结论:

  • 深度学习诊断系统可以改善脑瘤的分类和细分,
  • 这种基于网络的工具具有用户友好的界面,适合临床放射学工作流程.
  • 可解释的AI技术增强了神经瘤学中的深度学习的临床实用性.