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

FET-FIDS: a federated enhanced transformer-based framework for privacy-preserving network intrusion detection.

Scientific reports·2026

Same author

A 24 GHz End-Fire Rod Antenna Based on a Substrate Integrated Waveguide.

Sensors (Basel, Switzerland)·2025

Same author

Breast Cancer Diagnosis Using Virtualization and Extreme Learning Algorithm Based on Deep Feed Forward Networks.

Biomedical engineering and computational biology·2024

Same author

Editorial for the Special Issue on Recent Advances in Microwave Components and Devices.

Micromachines·2024

Same author

An Electro-Oculogram (EOG) Sensor's Ability to Detect Driver Hypovigilance Using Machine Learning.

Sensors (Basel, Switzerland)·2023

Same author

Multi-Process Remora Enhanced Hyperparameters of Convolutional Neural Network for Lung Cancer Prediction.

Biomedicines·2023

Search research articles

相关实验视频

Updated: Jul 29, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

基于En-DeNet的细分和分级模块化网络分类,用于肝癌诊断.

Suganeshwari G¹, Jothi Prabha Appadurai², Balasubramanian Prabhu Kavin³

¹School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India.

|May 27, 2023

概括

本研究介绍了渐变模块化网络 (GraMNet),用于CT扫描中自动化肝脏和瘤细分. 格拉姆网提供高效,低计算的深度学习,以更快,更准确的肝癌诊断.

关键词:

癌症的诊断癌症的诊断计算机断层扫描 (CT) 是一种计算机断层扫描.编码器解码器网络渐进式模块化网络是一个渐进式模块化网络.肝脏细分细分肝脏的细分

更多相关视频

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

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

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

Published on: October 10, 2018

相关实验视频

Last Updated: Jul 29, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

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

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

Published on: October 10, 2018

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:

肝癌是全球主要的健康问题,在患病率上排名第六.
计算机断层扫描 (CT) 扫描对于肝癌诊断至关重要,但手动分析是耗时的.
深度学习显示出在CT图像中对肝脏和瘤的细分自动化的前景.

研究的目的:

开发一种自动化的深度学习系统,从CT扫描中对肝脏和瘤进行细分.
为了减少肝癌诊断所需的时间和劳动力.
为了提高肝脏瘤细分的效率和准确性.

主要方法:

开发了一个使用基于UNet和Efficient.Net的编码解码网络 (En-DeNet) 的深度学习系统.
实施了专门的预处理技术,包括多通道图像,降噪和对比度增强.
提出了具有模块化子网的渐进模块化网络 (GraMNet),以优化培训和降低计算成本.

主要成果:

与LiTS和3DIRCADb01等基准相比,GraMNet在细分和分类任务中表现出了最先进的性能.
与传统的深度学习架构相比,提出的GraMNet实现了较低的计算难度.
格拉姆网表现出更快的训练,更低的内存消耗和更快的图像处理.

结论:

在CT成像中,GraMNet提供了一种高效有效的深度学习解决方案,用于CT成像中自动化肝脏和瘤细分.
格拉姆网的模块化方法优化了网络性能和资源利用.
该系统有可能显著加速肝癌诊断并改善患者的治疗结果.