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

Ultrasonography01:17

Ultrasonography

4.5K
Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called...
4.5K

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

Enhancing EEG Decoding with Selective Augmentation Integration.

Sensors (Basel, Switzerland)·2026
Same author

Self-supervised learning-based underwater acoustical signal classification via mask modeling.

The Journal of the Acoustical Society of America·2023
Same author

HEROHE Challenge: Predicting HER2 Status in Breast Cancer from Hematoxylin-Eosin Whole-Slide Imaging.

Journal of imaging·2022
Same author

KnowRU: Knowledge Reuse via Knowledge Distillation in Multi-Agent Reinforcement Learning.

Entropy (Basel, Switzerland)·2021
Same author

Learning to Cooperate via an Attention-Based Communication Neural Network in Decentralized Multi-Robot Exploration.

Entropy (Basel, Switzerland)·2020
Same author

Multistructure-Based Collaborative Online Distillation.

Entropy (Basel, Switzerland)·2020
Same journal

Semantic Explanation for Malaria Diagnosis: Comparing Human and Machine Generated Annotations for <i>Plasmodium</i> Species and Life-Stage Features.

IEEE open journal of engineering in medicine and biology·2026
Same journal

An Improved Beta Burst Extraction for Chip-Based Deep Brain Stimulation With Real-Time Model Updating.

IEEE open journal of engineering in medicine and biology·2026
Same journal

Transcranial Temporal Interference Stimulation: A Brief Review of Architectures, Circuits, and Application Challenges.

IEEE open journal of engineering in medicine and biology·2026
Same journal

An Intra-Body Power Transfer System via Localized Capacitive Coupling.

IEEE open journal of engineering in medicine and biology·2026
Same journal

Shared and Individual Resting-State MEG Network Signatures of Tinnitus Revealed by Holistic Graph Learning.

IEEE open journal of engineering in medicine and biology·2026
Same journal

Stain Consistency Learning: Handling Stain Variation for Automatic Digital Pathology Segmentation.

IEEE open journal of engineering in medicine and biology·2026
查看所有相关文章

相关实验视频

Updated: Jun 28, 2025

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

1.8K

通过自我监督学习进行基于面具建模的超声波图像分类.

Kele Xu1, Kang You2, Boqing Zhu1

  • 1National University of Defense Technology Changsha 410073 China.

IEEE open journal of engineering in medicine and biology
|April 12, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种自我监督的超声数据分析预训练方法,使用面具建模来学习未标记图像的特征. 该方法有效地处理具有挑战性的超声波数据,并提高分类性能.

关键词:
预培训 预培训 预培训蒙面的模特表演自主监督的自我监督超声波图像 超声波图像 超声波图像

更多相关视频

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

2.7K
High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning
09:31

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

3.0K

相关实验视频

Last Updated: Jun 28, 2025

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

1.8K
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

2.7K
High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning
09:31

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

3.0K

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算机视觉 计算机视觉

背景情况:

  • 深度学习方法主导超声波数据分析,但需要广泛的注释数据集.
  • 超声波图像中的斑点噪声和工件造成了困难的分类挑战.
  • 现有的方法难以获得标记的超声波数据.

研究的目的:

  • 开发一种自我监督的超声波数据分析预训练方法.
  • 解决对超声波深度学习中大型注释数据集的需求.
  • 通过处理硬实例和文物来改进超声波图像分类.

主要方法:

  • 一种基于对超声数据的面具建模的新型预训练方法.
  • 研究三种掩盖策略:随机,垂直和水平掩盖.
  • 实施硬样本采矿策略,以管理困难的超声波图像.

主要成果:

  • 提出的方法成功地从未标记的超声数据集中提取了代表性特征.
  • 与最先进的方法相比,这种方法在超声波图像分类中表现出卓越的性能.
  • 即使存在硬实例和成像工件,也可以实现有效的特征提取.

结论:

  • 自主监督的面具建模是超声波数据预训练的可行和有效方法.
  • 该方法减少了对人类注释的依赖,使得大量未标记的超声数据的使用成为可能.
  • 拟议的战略提高了超声波图像分类模型的稳定性和准确性.