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

Parallel Processing

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...

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

Development and validation of the Korean version of the Modified Japanese Orthopedic Association Score: a prospective questionnaire study.

Asian spine journal·2026

Same author

Systematic characterization of purple acid phosphatases provides targets for improving phosphorus use efficiency in cotton.

Functional & integrative genomics·2026

Same author

Design and development of a 1-bit dual-mode metasurface for sub-6 GHz wireless communication systems.

Scientific reports·2026

Same author

A lightweight hybrid framework for real-time data refinement in resource-constrained underwater and underground wireless sensor networks.

Scientific reports·2026

Same author

Genes and physiological strategies in bacterial antibiotic resistance.

Frontiers in microbiology·2026

Same author

HybridTrust: on-device federated learning with crypto-agile security for legacy and quantum-safe medical devices.

Scientific reports·2026

Search research articles

相关实验视频

Updated: Jan 16, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

双边合作流与多模式关注网络,用于准确的多片细分.

Rahim Khan¹, Nada Alzaben², Yousef Ibrahim Daradkeh³

¹College of Information and Communication Engineering, Harbin Engineering University, Harbin, 150001, China.

Scientific reports

|October 1, 2025

概括

此摘要是机器生成的。

使用新的双边卷积多重注意网络 (BiCoMA) 精确细分结直肠多,可以改善早期癌症检测. 这种深度学习模型有效地整合了全球和本地特征,以便在结肠镜图像中精确识别多重体.

关键词:

混合CNN变压器的混合变压器多重注意力多重注意力多个尺度的注意力.聚合物细分的聚合物细分.语义融合是指语义上的融合.视觉智能是一种视觉智能.

更多相关视频

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

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

相关实验视频

Last Updated: Jan 16, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

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

科学领域:

医学成像和人工智能医学成像和人工智能
计算病理学计算病理学
胃肠病学胃肠病学

背景情况:

在结肠镜检查中对结肠直肠多的准确细分对于早期癌症检测和预防至关重要.
现有的细分方法面临的挑战是聚的多样性 (大小,形态,纹理) 和临床使用的计算效率.

研究的目的:

提出一种新的双流深度学习架构,双边卷积多重注意网络 (BiCoMA),用于准确的结直肠多片细分.
通过有效地整合全球上下文信息和本地空间细节来提高细分性能.

主要方法:

开发了一种双流架构 (BiCoMA),结合了卷积神经网络 (ConvNeXt V2 大) 和视觉变压器 (金字塔视觉变压器).
集成空间改进 (SR) 和通道改进 (CR) 模块与非局部注意力 (NLA) 功能增强.
采用了带有金字塔注意力区块 (PAB) 和卷积区注意力模块 (CBAM) 的等级解码器,用于特征融合和歧视.

主要成果:

在五个基准数据集 (Endoscene,ClinicDB,ColonDB,ETIS,Kvasir-SEG) 上,BiCoMA实现了最先进的性能.
该网络在多种多呈现形式中展示了优越的概括能力.
拟议的架构保持了实用的计算效率,适合实时临床应用.

结论:

双边卷积多重注意网络 (BiCoMA) 在自动化结直肠多片细分方面取得了重大进展.
毕科马的混合方法有效地解决了与多变异性和计算需求相关的挑战.
这项技术有望提高基于结肠镜的癌症查的准确性和效率.