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

Targeting Warburg effect in Chinese hamster ovary cell culture with pyruvate dehydrogenase kinase inhibitors.

Biotechnology progress·2026

Same author

Osmolyte-Based Formulations for Enhanced Thermal Stability of mRNA Drug Substance: A Systematic Screening and Optimization Study.

Pharmaceutical research·2026

Same author

Structural Elucidation of Fc- and Fab-Associated <i>N</i>-Glycans in Cetuximab Using Protein A-Assisted Domain-Resolved Glycan Profiling Using Mass Spectrometry.

Journal of the American Society for Mass Spectrometry·2026

Same author

Artificial Intelligence for Molecular Subtyping in Unresectable Gallbladder Cancer: A Proof-of-Concept Study for CT-based HER2 Status Prediction.

Journal of clinical and experimental hepatology·2026

Same author

Bioinformatic pipeline to identify potential therapeutic targets with subsequent isolation and characterization of novel human anti- DDR1 antibodies.

Scientific reports·2026

Same author

Rapid Identification of Counterfeit Biopharmaceuticals using Portable Fourier Transform Infrared Spectroscopy.

AAPS PharmSciTech·2026

Search research articles

相关实验视频

Updated: Jun 13, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

使用多实例学习的弱监督的大规模胰腺癌检测.

Shyamapada Mandal¹, Keerthiveena Balraj², Hariprasad Kodamana^1,2

¹Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, India.

Frontiers in oncology

|September 13, 2024

概括

此摘要是机器生成的。

一个新的两阶段深度学习模型在CT扫描上准确检测胰腺瘤. 这一进步为胰腺癌的早期检测提供了改进,这种疾病缺乏有效的查方法.

关键词:

功能提取特性提取图像分割图像细分图像细分医疗图像分析分析多级别的学习多级别的学习.胰腺癌是一种癌症.

更多相关视频

Utilizing High Resolution Ultrasound to Monitor Tumor Onset and Growth in Genetically Engineered Pancreatic Cancer Models

Utilizing High Resolution Ultrasound to Monitor Tumor Onset and Growth in Genetically Engineered Pancreatic Cancer Models

Published on: April 7, 2018

Computer-assisted Large-scale Visualization and Quantification of Pancreatic Islet Mass, Size Distribution and Architecture

Computer-assisted Large-scale Visualization and Quantification of Pancreatic Islet Mass, Size Distribution and Architecture

Published on: March 4, 2011

相关实验视频

Last Updated: Jun 13, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Utilizing High Resolution Ultrasound to Monitor Tumor Onset and Growth in Genetically Engineered Pancreatic Cancer Models

Utilizing High Resolution Ultrasound to Monitor Tumor Onset and Growth in Genetically Engineered Pancreatic Cancer Models

Published on: April 7, 2018

Computer-assisted Large-scale Visualization and Quantification of Pancreatic Islet Mass, Size Distribution and Architecture

Computer-assisted Large-scale Visualization and Quantification of Pancreatic Islet Mass, Size Distribution and Architecture

Published on: March 4, 2011

科学领域:

医疗成像医学成像
人工智能的人工智能
在瘤学瘤学.

背景情况:

早期发现胰腺癌仍然是一个重大的临床挑战.
目前胰腺癌的诊断方法有限,往往导致晚期诊断和治疗效率降低.

研究的目的:

开发和评估一种新的两阶段深度学习模型,用于使用计算机断层扫描 (CT) 图像早期检测胰腺瘤.
与现有方法相比,提高胰腺癌识别的准确性和效率.

主要方法:

开发了一个两阶段弱监督的深度学习模型.
第一个阶段利用nnU-Net对纪念斯隆凯特林癌症中心 (MSKCC) 数据进行胰腺细分.
第二阶段使用了亨利·福特健康 (HFH) 数据上的多实例学习分类器来检测瘤.

主要成果:

拟议的模型实现了高性能指标:准确度为0.907,灵敏度为0.905,特异性为0.908,AUC (ROC) 为0.903.
两阶段的框架表明,在CT图像中,胰腺瘤与非瘤胰腺的区分能力有所提高.
与其他报告的研究相比,该模型显示显著提高了性能.

结论:

开发的两阶段深度学习架构显示了使用CT成像增强胰腺瘤检测的重大前景.
这种方法在应对早期胰腺癌诊断的挑战方面提供了潜在的突破.
进一步验证和整合到临床工作流程中可以改善患者的治疗结果.