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

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...

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

A Hybrid Deep Learning Framework for Automated Dental Disorder Diagnosis from X-Ray Images.

Journal of clinical medicine·2026
Same author

EfficientNet-B3-Based Automated Deep Learning Framework for Multiclass Endoscopic Bladder Tissue Classification.

Diagnostics (Basel, Switzerland)·2025
Same author

FracFusionNet: A Multi-Level Feature Fusion Convolutional Network for Bone Fracture Detection in Radiographic Images.

Diagnostics (Basel, Switzerland)·2025
Same author

Enhancing Early Detection of Oral Squamous Cell Carcinoma: A Deep Learning Approach with LRT-Enhanced EfficientNet-B3 for Accurate and Efficient Histopathological Diagnosis.

Diagnostics (Basel, Switzerland)·2025
Same author

A Hybrid Model of Feature Extraction and Dimensionality Reduction Using ViT, PCA, and Random Forest for Multi-Classification of Brain Cancer.

Diagnostics (Basel, Switzerland)·2025
Same author

Ensemble deep learning for Alzheimer's disease diagnosis using MRI: Integrating features from VGG16, MobileNet, and InceptionResNetV2 models.

PloS one·2025
Same journal

Correction: Luca et al. Global and Regional Diagnostic Results of Progress Toward Cervical Cancer Elimination, According to the WHO Strategy: A Systematic Literature Review with Narrative Synthesis. <i>Diagnostics</i> 2026, <i>16</i>, 1224.

Diagnostics (Basel, Switzerland)·2026
Same journal

Association Between Systemic Inflammatory Response Biomarkers and Disease Activity in Systemic Lupus Erythematosus: A Multi-Center Retrospective Study.

Diagnostics (Basel, Switzerland)·2026
Same journal

Vertebrogenic Low Back Pain and Basivertebral Nerve Ablation: A Review of Mechanisms, Imaging-Driven Selection, and Clinical Outcomes.

Diagnostics (Basel, Switzerland)·2026
Same journal

Multivalvular Carcinoid Heart Disease: The Role of Echocardiography in Diagnosis and Selection for Heterotopic Bicaval Valve Implantation.

Diagnostics (Basel, Switzerland)·2026
Same journal

Data-Efficient and Explainable Multimodal Survival Prediction in NSCLC Using Deep Image Embeddings, Clinical Variables, and Gradient-Boosted Trees.

Diagnostics (Basel, Switzerland)·2026
Same journal

Anomalous Left Coronary Artery from the Pulmonary Artery: Cinematic Volume Rendering Technique for Enhanced Anatomic Visualization.

Diagnostics (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jun 26, 2026

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
06:08

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

1.1K

一个具有高级功能融合的多阶段混合学习模型,用于增强前列腺癌分类.

Sameh Abd El-Ghany1, A A Abd El-Aziz1

  • 1Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.

Diagnostics (Basel, Switzerland)
|December 30, 2025
PubMed
概括
此摘要是机器生成的。

一个新的混合学习模型结合了深度和手工制作的功能,显著改善了使用MRI的前列腺癌 (PCa) 诊断. 这种先进的方法实现了高精度,为临床决策支持提供了可靠的工具.

关键词:
深度学习是一种深度学习.面向梯度的直径图. 面向梯度的直径图磁共振成像技术的使用前列腺癌是前列腺癌.单一价值分解分解的方法支持矢量机器的支持矢量机器.横平面前列腺数据集

更多相关视频

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:05

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

1.1K
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

741

相关实验视频

Last Updated: Jun 26, 2026

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
06:08

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

1.1K
Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:05

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

1.1K
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

741

科学领域:

  • 医疗成像医学成像
  • 人工智能在医学中的应用
  • 在瘤学瘤学.

背景情况:

  • 前列腺癌 (PCa) 是男性癌症死亡的主要原因,由于成像变异性,它存在诊断挑战.
  • 磁共振成像 (MRI) 对于PCa检测至关重要,但准确的分类需要整合各种特征类型.
  • 将深度学习特征 (CNN) 与手工描述符 (HOG) 结合起来,对于增强计算机辅助诊断至关重要.

研究的目的:

  • 开发一个多阶段的混合学习模型,以使用MRI改进PCa诊断.
  • 调查特征减少和分类技术,以获得最佳的诊断性能.
  • 提高前列腺癌计算机辅助诊断的准确性和可靠性.

主要方法:

  • 集成深度特征从CNN与手工制作的纹理描述器 (例如,HOG).
  • 在合并的特征空间上使用了像单数值分解 (SVD) 这样的缩小维度的技术.
  • 他们对各种机器学习分类器进行了基准测试,并使用k-fold交叉验证验证该框架.

主要成果:

  • 混合模型显著优于个人深度或手工制作的特征方法.
  • 在TPP数据集上实现了特殊的性能指标:99.74%的准确性,99.87%的特异性,99.87%的精度,99.61%的灵敏性和99.74%的F1得分.
  • 在二进制分类任务中表现出卓越的诊断能力.

结论:

  • 拟议的混合模型为PCa诊断提供了强大的和可通用的解决方案.
  • 互补特征的有效整合,维度降低和优化分类提高了诊断准确度.
  • 该模型显示了集成到临床决策支持系统的强大潜力.