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

Tumor Progression02:07

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
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,...
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
Tumor Progression02:07

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
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,...
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...

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

Multimodal Fusion of Endoscopic and Histopathological Images for Lesion Detection Using Hybrid Deep Learning.

Current medical imaging·2026
Same author

RETRACTED: Srivastava et al. Match-Level Fusion of Finger-Knuckle Print and Iris for Human Identity Validation Using Neuro-Fuzzy Classifier. <i>Sensors</i> 2022, <i>22</i>, 3620.

Sensors (Basel, Switzerland)·2026
Same author

Deep learning-based region merging with adaptive threshold optimization for building segmentation in remote sensing images.

PloS one·2026
Same author

Stacking Deep Neural Networks to Detect Multiple Types of Cardiac Arrhythmias.

IEEE journal of biomedical and health informatics·2026
Same author

Honey yield prediction and neonicotinoid risk assessment utilizing a machine learning framework in smart agriculture.

Scientific reports·2026
Same author

Correction: Ergonomic assessment of a multi-joint actuated lower extremity exoskeleton to assist dynamic lifting and carrying tasks.

Scientific reports·2026
Same journal

Peripheral B-cell receptor repertoire predicts immune-related adverse events following immune checkpoint inhibitor therapy in advanced renal cell carcinoma.

Scientific reports·2026
Same journal

Effects of black soldier fly (Hermetia illucens L.) larvae zoocompost on the mineral element content of blue honeysuckle berries.

Scientific reports·2026
Same journal

Investigation on absorption refrigeration performance of R1243zf with imidazolium ionic liquid as the working pairs.

Scientific reports·2026
Same journal

DeepTriage-CN: integrating clinical text with vital signs for emergency department admission prediction in an aging population.

Scientific reports·2026
Same journal

Gold nanoparticles as dual-action antiviral agents: disruption of SARS-CoV-2 viral envelopes and RNA integrity.

Scientific reports·2026
Same journal

Comparison of capillary microsampling and venous blood for multi-pathogen serosurveillance.

Scientific reports·2026
查看所有相关文章

相关实验视频

Updated: Jul 1, 2026

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
14:14

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

9.0K

先进的动态组合框架,具有可解释性驱动的洞察力,用于跨数据集的精确脑瘤分类.

Retinderdeep Singh1, Sheifali Gupta1, Ashraf Osman Ibrahim2,3

  • 1Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India.

Scientific reports
|August 8, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种先进的深度学习系统,用于从MRI扫描中精确地分类脑瘤. 它达到99.4%的准确性,提高了医疗成像中的诊断可靠性和解释性.

关键词:
大脑瘤是什么?动态重量是动态的重量.有效的网 效率的网组合模型模型组合模型可解释的人工智能这就是ResNet ResNet.

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

7.3K
Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.3K

相关实验视频

Last Updated: Jul 1, 2026

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
14:14

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

9.0K
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

7.3K
Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.3K

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 由于瘤的多样性和人为错误,精确的脑瘤检测具有挑战性.
  • 现有的诊断方法需要提高精度和可靠性.

研究的目的:

  • 开发一种新的集体深度学习系统,使用MRI数据准确地分类脑瘤.
  • 为了提高诊断准确度,并在脑瘤检测中提供解释性.

主要方法:

  • 一个整体框架,整合了微调的卷积神经网络 (CNN),ResNet-50和EfficientNet-B5.
  • 适应性动态重量分布和定制加权交叉损失函数.
  • 可解释的AI (XAI) 技术 (Grad-CAM,SHAP,SmoothGrad,LIME) 和基于的不确定性分析.

主要成果:

  • 实现了高分类准确率:99.4% (测试),99.48% (验证) 和99.31% (交叉数据集).
  • 通过XAI技术证明了强大的解释性.
  • 量化预测信心与0.3093的平均,识别不确定的预测.

结论:

  • 拟议的深度学习框架为脑瘤分类提供了高精度,稳定性和可解释性.
  • 它显示了集成到自动诊断系统的巨大潜力,减少了错误.
  • 该系统有效地解决了阶级不平衡,并改善了模型通用化.