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相关概念视频

Structural Classification of Joints01:20

Structural Classification of Joints

3.6K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
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Shape and Texture of Coarse Aggregate01:25

Shape and Texture of Coarse Aggregate

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Aggregate shape is classified based on the relative sharpness or roundness of the edges and corners. This classification includes categories like rounded, angular, elongated, and flaky, each with specific characteristics. Rounded aggregates, fully shaped by attrition, are typical of river or seashore gravel, while angular aggregates, such as crushed rock, have well-defined edges. Aggregates that are elongated and flaky are less desirable, as they can reduce the workability and strength of...
238
Functional Classification of Joints01:09

Functional Classification of Joints

4.2K
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...
4.2K
Introduction to Joints00:58

Introduction to Joints

3.1K
The adult human body usually has 206 bones, and except for the hyoid bone in the neck, each bone is connected to at least one other bone. Joints are the location where bones come together. Many joints allow for movement between the bones. At these joints, the articulating surfaces of the adjacent bones can move smoothly against each other. However, the bones of other joints may be joined by connective tissue or cartilage. These joints are designed for stability and provide little or no...
3.1K
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

98
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
98
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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相关实验视频

Updated: Jul 24, 2025

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

Published on: November 30, 2022

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STNet:通过以知识为导向的图像识别双流网络进行形状和纹理联合学习.

Xijing Wang1, Hongcheng Han1, Mengrui Xu1,2

  • 1National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China.

Frontiers in neuroscience
|July 3, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个新的双流网络,通过增强形状特征表示来改进医疗图像分析. 该方法有效地区分形状和纹理,在黑色素瘤识别任务中表现优于现有的算法.

关键词:
类似于大脑的信息处理.计算机辅助诊断是指计算机辅助的诊断.功能融合功能融合功能图像识别功能 图像识别功能共同学习 共同学习两个流网络网络的两个流.

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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相关实验视频

Last Updated: Jul 24, 2025

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

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Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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科学领域:

  • 计算机视觉 计算机视觉
  • 医学图像分析 医学图像分析
  • 人工智能的人工智能

背景情况:

  • 医疗图像分析通常由于预训练偏见而与形状特征表示作斗争.
  • 使用像ImageNet这样的数据集的当前方法可以改善纹理,但忽略了关键的形状信息.
  • 准确的形状分析对于许多医学成像诊断任务至关重要.

研究的目的:

  • 通过使用一种新的双流网络,在医学图像分析中增强形状特征表示.
  • 提高计算机辅助诊断系统的准确性和稳定性.
  • 解决现有的预训练模型在捕捉形状特征方面的局限性.

主要方法:

  • 提出了一个以形状和纹理为偏见的双流网络,具有多任务联合学习.
  • 利用金字塔分组的卷积来增强纹理和可变形卷积来提取形状特征.
  • 实现了基于频道注意力的特征选择模块,以实现有效的特征融合,以及用于样本不平衡的不对称损失函数.

主要成果:

  • 拟议的方法在ISIC-2019和XJTU-MM数据集上的黑色素瘤识别任务中表现出卓越的性能.
  • 对皮肤镜和病理图像识别的实验结果验证了该方法的有效性.
  • 与现有的算法相比,该网络成功增强了形状特征表示.

结论:

  • 基于形状和纹理的双流网络通过优先考虑形状特征,显著改善了医疗图像分析.
  • 该方法为需要详细形状分析的任务提供了强大的解决方案,例如损伤识别.
  • 这种方法推进了以知识为导向的医学图像分析和计算机辅助诊断.