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

Endoscopic Procedures II: Colonoscopy01:25

Endoscopic Procedures II: Colonoscopy

183
The colon, or large intestine, is the final segment of the digestive system. Its primary functions include absorbing water and vitamins produced by gut bacteria and transforming waste from liquid to solid to form stool. In adults, the large intestine is approximately 5 feet long and consists of four main sections:
183
Endoscopic Procedures IV: Sigmoidoscopy and Laproscopy01:26

Endoscopic Procedures IV: Sigmoidoscopy and Laproscopy

170
Sigmoidoscopy and laparoscopy are distinct medical procedures that enable physicians to internally inspect different parts of the GI tract. Although they serve different purposes, each is essential for diagnosing and, in some cases, treating various medical conditions.
Sigmoidoscopy
Sigmoidoscopy is a diagnostic procedure that uses a flexible sigmoidoscope equipped with a light source and camera to examine the rectum and sigmoid colon. The procedure involves inserting the tube through the anus...
170
Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy01:26

Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy

142
This lesson explores three gastrointestinal imaging techniques: radionuclide testing, colonic transit studies, and virtual colonoscopy.
Radionuclide Testing
Radionuclide testing is a sophisticated medical technique for assessing gastrointestinal motility. It focuses on gastric emptying and colonic transit time. Radioactive markers track the movement of food through the digestive system, providing insights into gastrointestinal disorders.
In gastric emptying studies, a meal's liquid and...
142

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

Trainable clustering framework for spatial transcriptomics.

Bioinformatics advances·2026
Same author

TextEconomizer: Enhancing lossy text compression with denoising transformers and entropy coding.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

The good, the bad, and the ugly: opportunities, challenges, and pitfalls in spatial proteomics modeling.

Briefings in bioinformatics·2026
Same author

Spatial information matters: are traditional imputation methods effective for spatial transcriptomics data?

Briefings in bioinformatics·2026
Same author

Integrating lightweight convolutional neural network with entropy-informed channel attention and adaptive spatial attention for OCT-based retinal disease classification.

Computers in biology and medicine·2025
Same author

CoDNet: controlled diffusion network for structure-based drug design.

Bioinformatics advances·2025
Same journal

A computational model of chemically- and mechanically-induced thrombus formation in cerebral aneurysms.

Computers in biology and medicine·2026
Same journal

An improved catch fish optimization based deep learning model for Parkinson disease classification using EEG signal.

Computers in biology and medicine·2026
Same journal

Assessing the robustness of evaluation metrics for synthetic ECG signal quality.

Computers in biology and medicine·2026
Same journal

Integrating stemness and epithelial-mesenchymal transition signatures with machine learning identifies RUNX1 as a therapeutic vulnerability in colorectal cancer.

Computers in biology and medicine·2026
Same journal

Differential regional textural attributes of tongue in normal and acidity patients in the light of traditional Chinese medicine.

Computers in biology and medicine·2026
Same journal

SC-MSDNet: Spatial-consistent multi-view self-distillation for retinal OCT classification.

Computers in biology and medicine·2026
查看所有相关文章

相关实验视频

Updated: Sep 9, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

2.9K

使用DeepLabV3+的结肠镜图像中的多体细分

Al Mohimanul Islam1, Sadia Shakiba Bhuiyan1, Mysun Mashira1

  • 1Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh.

Computers in biology and medicine
|August 28, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种增强的DeepLabv3++模型,用于精确分离结肠镜图像. 改进的模型显著减少了细分错误,有助于早期发现结直肠癌.

关键词:
注意聚合结肠镜的图像深度实验室V3+有效的NetV2S多级特征提取多胞体细分

更多相关视频

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment
10:39

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

Published on: May 24, 2022

2.4K
Author Spotlight: Optimization of Ultrashort Peptide Matrices for Colorectal Cancer Organoids
10:23

Author Spotlight: Optimization of Ultrashort Peptide Matrices for Colorectal Cancer Organoids

Published on: May 3, 2024

1.0K

相关实验视频

Last Updated: Sep 9, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

2.9K
A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment
10:39

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

Published on: May 24, 2022

2.4K
Author Spotlight: Optimization of Ultrashort Peptide Matrices for Colorectal Cancer Organoids
10:23

Author Spotlight: Optimization of Ultrashort Peptide Matrices for Colorectal Cancer Organoids

Published on: May 3, 2024

1.0K

科学领域:

  • 医学成像
  • 人工智能
  • 计算机视觉

背景情况:

  • 结肠直肠癌是全球癌症死亡的主要原因.
  • 在结肠镜检查中精确分片对于早期诊断至关重要.
  • 现有的深度学习模型在小细节和多尺度特征表示方面存在困难.

研究的目的:

  • 开发一个增强的DeepLabv3++模型,以改善结肠镜图像中的息肉细分.
  • 为了提高聚检测的精度和稳定性.
  • 为了更好的临床决策,减少细分错误.

主要方法:

  • 在编码器中使用EfficientNetV2S进行精细的特征提取.
  • 综合多层次金字塔聚合 (MSPP) 和并行注意力聚合区 (PAAB) 模块.
  • 实现了重新设计的解码器,用于增强特征转换和细分地图生成.

主要成果:

  • 达到了高的子系数: 96. 20% (CVC-ColonDB), 96. 54% (CVC-ClinicDB) 和 96. 08% (Kvasir-SEG).
  • 在聚细分方面表现优于几种最先进的模型.
  • 与基线DeepLabv3+相比,所有大小的息肉的细分错误 (错误阳性/阴性) 显著减少.

结论:

  • 增强的DeepLabv3++模型在结肠镜聚细分方面表现出卓越的性能.
  • 整合MSPP和重新设计的解码器提高了模型捕获多尺度和定向特征的能力.
  • 这一进步对于精确的结核划分和结直肠癌查的临床决策至关重要.