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

Heart Sounds01:15

Heart Sounds

3.2K
Heart sounds are generated by the turbulence in blood flow due to the closing of heart valves. These sounds are best perceived slightly away from the valves, where the blood flow disseminates the sound.
Auscultation is the process of listening to these internal body sounds using a stethoscope. The heart produces four types of sounds, but only two—S1 and S2—can usually be heard with a stethoscope.
S1, also known as the "lub" sound, is caused by the closure of atrioventricular (A-V)...
3.2K
Parallel Processing01:20

Parallel Processing

626
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...
626
Classification of Signals01:30

Classification of Signals

1.3K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.3K

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 Functional Shape Framework for the Detection of Multiple Sclerosis Using Optical Coherence Tomography Images.

Sensors (Basel, Switzerland)·2026
Same author

A Multi-Level Self-Distillation-Based Unified Tracker for Efficient RGB-T Tracking.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Resource-Efficient and Layer Interdependence-Aware CNN Pruning Leveraging Filter Replacement.

IEEE transactions on neural networks and learning systems·2026
Same author

Adjuvant tislelizumab, lenvatinib, and capecitabine for resected biliary tract cancer: a prospective phase II trial.

BMC medicine·2026
Same author

Quantitative analysis of granules moisture content within a fluidized bed drying process using simultaneously Near-Infrared and Raman Spectroscopy combined with multivariate models.

International journal of pharmaceutics·2026
Same author

ECG-EmotionNet: Nested Mixture of Expert (NMoE) Adaptation of ECG-Foundation Model for Driver Emotion Recognition.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Correction: Komatsu et al. Three-Dimensional Visualization and Detection of the Pulmonary Venous-Left Atrium Connection Using Artificial Intelligence in Fetal Cardiac Ultrasound Screening. <i>Bioengineering</i> 2026, <i>13</i>, 100.

Bioengineering (Basel, Switzerland)·2026
Same journal

Comparison of CO<sub>2</sub> Laser and Microdebrider in the Surgical Treatment of Pediatric Recurrent Respiratory Papillomatosis: A Retrospective Analysis.

Bioengineering (Basel, Switzerland)·2026
Same journal

Toward More Translational Tumor Models: Breast dECM-Based 3D Systems Capture Native Microenvironmental Cues.

Bioengineering (Basel, Switzerland)·2026
Same journal

Postural Stability Changes During the 4 Phases of the Half Squat: Kinematics Profile of the Center of Pressure and Center of Mass in High-Performance Weightlifters-A Pilot Study.

Bioengineering (Basel, Switzerland)·2026
Same journal

Definite Implant Position as Novel Readout for Effectiveness of Ridge Preservation Indicates to Beneficial Effect of Combined Treatment with Platelet-Rich Fibrin (PRF) and Xenogenic Biomaterial in Bone Regeneration.

Bioengineering (Basel, Switzerland)·2026
Same journal

Trueness and Precision of Intraoral Scanners for 3D-Printed Orthodontic Models with Attachments: An In Vitro Comparative Study.

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

相关实验视频

Updated: Jan 13, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

2.3K

基于注意力融合的双流视觉变压器用于心脏声音分类.

Kalpeshkumar Ranipa1, Wei-Ping Zhu1, M N S Swamy1

  • 1Department of Electrical and Computer Engineering, Concordia University, Montreal, QC H3G 1M8, Canada.

Bioengineering (Basel, Switzerland)
|October 29, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种基于注意力融合的双流视觉转换器 (AFTViT) 用于心脏声音分类 (HSC). 新的AFTViT架构提高了诊断心血管疾病的准确性.

关键词:
聚合注意力 聚合注意力深度学习是一种深度学习.心脏声音分类心脏声音分类视觉变压器 视觉变压器

更多相关视频

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

732

相关实验视频

Last Updated: Jan 13, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

2.3K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

732

科学领域:

  • 人工智能的人工智能
  • 生物医学工程 生物医学工程
  • 心脏病学 心脏病学

背景情况:

  • 心脏声音分类 (HSC) 对于心血管疾病诊断至关重要.
  • 现有的方法经常使用单流架构,缺少多分辨率功能的好处.
  • 当前的多流方法在融合过程中扎于跨模式交互和信息丢失.

研究的目的:

  • 开发一种新的基于注意力融合的双流视觉转换器 (AFTViT),用于增强心脏声音分类.
  • 为了有效地捕捉和整合心脏声信号中的多分辨率和跨模式特征.
  • 克服现有的高能电池架构中常规聚变方法的局限性.

主要方法:

  • 提出了一个使用二维 mel-cepstral 域特征的 AFTViT 架构.
  • 采用基于视觉变压器 (ViT) 的编码器来捕获远程依赖和多尺度的上下文信息.
  • 引入了一个新的注意力块,用于跨上下文特征的功能级整合.

主要成果:

  • 与基于CNN的最新方法相比,AFTViT架构在PhysioNet2016和PhysioNet2022数据集上表现出卓越的性能.
  • 在心声分类任务中获得更高的准确性.
  • 通过整合跨背景信息,注意力融合机制有效地增强了特征表示.

结论:

  • AFTViT框架显示了提高心脏声音分类准确性的巨大潜力.
  • 这种方法为早期诊断心血管疾病提供了一个有希望的工具.
  • 该研究强调了基于注意力的融合在生物医学信号处理的多流视觉变压器架构中的有效性.