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

Aggregates Classification01:29

Aggregates Classification

317
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
317
Functional Classification of Joints01:09

Functional Classification of Joints

4.1K
Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
4.1K
Classification of Systems-I01:26

Classification of Systems-I

184
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
184
Classification of Signals01:30

Classification of Signals

455
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...
455
Classification of Systems-II01:31

Classification of Systems-II

144
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
144
Force Classification01:22

Force Classification

1.2K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.2K

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

Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge.

Medical image analysis·2024
Same author

Attentional decoder networks for chest X-ray image recognition on high-resolution features.

Computer methods and programs in biomedicine·2024
Same author

Analyzing to discover origins of CNNs and ViT architectures in medical images.

Scientific reports·2024
Same author

Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge.

ArXiv·2023
Same author

Deep learning using computed tomography to identify high-risk patients for acute small bowel obstruction: development and validation of a prediction model : a retrospective cohort study.

International journal of surgery (London, England)·2023
Same author

Effect of Contrast Level and Image Format on a Deep Learning Algorithm for the Detection of Pneumothorax with Chest Radiography.

Journal of digital imaging·2023

相关实验视频

Updated: Jun 28, 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

2.7K

用拼图拼图进行细粒度自主监督学习,用于医学图像分类.

Wongi Park1, Jongbin Ryu2

  • 1Department of Software, Ajou University, Republic of Korea.

Computers in biology and medicine
|April 18, 2024
PubMed
概括

很难对微妙的医疗病变进行分类. 细粒度自主监督学习 (FG-SSL) 方法使用等级块来改进医疗图像中的细粒度病变分类,优于现有方法.

科学领域:

  • 医学成像分析 医学成像分析
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 在医学图像中对细粒度病变进行分类,由于微妙的视觉差异,因此存在挑战.
  • 训练深层神经网络从这种微妙的差异中学习特征是很困难的.

研究的目的:

  • 引入一种新的细粒度自主监督学习 (FG-SSL) 方法,以改进医疗图像中微妙损伤的分类.
  • 增强医疗图像数据集中微妙差异的学习.

主要方法:

  • FG-SSL方法采用一个层次的块结构来进行渐进式学习.
  • 它强制执行细粒度拼图和规范原始图像之间的交叉相关性,以近似识别矩阵.
  • 层次块也应用于监督学习,以加强微妙差异的发现.

主要成果:

  • 拟议的FG-SSL方法与最先进的方法相比,表现良好.
  • 对包括ISIC2018,APTOS2019和ISIC2017在内的综合医疗图像识别数据集进行了实验.
  • 该方法不需要不对称的模型或负采样,并且对批量大小不敏感.

结论:

  • FG-SSL方法有效地解决了医学成像中细粒度病变分类的挑战.
关键词:
细粒度的医疗图像识别系统.这是一个拼图拼图.渐进式学习是一种渐进式学习.自主监督学习学习

更多相关视频

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.0K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

394

相关实验视频

Last Updated: Jun 28, 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

2.7K
Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.0K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

394
  • 使用等级块的渐进式学习方法显著改善了细微差异的识别.
  • 对于医疗图像分析任务,FG-SSL提供了强大而高效的解决方案.