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

Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an organic...
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Relative Frequency Distribution00:55

Relative Frequency Distribution

A relative frequency distribution is the proportion or fraction of times a value occurs in a data set. To find the relative frequencies, one can divide each frequency by the total number of data points in the sample. It is very similar to a regular frequency distribution, except that instead of reporting how many data values fall in a class, a relative frequency distribution reports the fraction of data values that fall in a class. These fractions or proportions are called relative frequencies...
¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
A broadband decoupling technique is used to simplify these complex, sometimes overlapping, signals. Broadband decoupling relies on a...
IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
The...
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.

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

Spectral CT-based habitat analysis for predicting pathologic response to neoadjuvant therapy in gastric cancer.

European radiology·2026
Same author

Preoperative prediction of lymphovascular and perineural invasion in locally advanced gastric cancer via CT habitat analysis and deep learning: A dual-center study.

European journal of radiology·2026
Same author

Prediction of efficacy and prognosis of PD‑1/PD‑L1 inhibitor combination chemotherapy for gastric cancer using an AI model based on dual‑energy CT: A multicenter study.

European journal of radiology·2026
Same author

Differential diagnosis of testicular embryonal rhabdomyosarcoma and testicular seminoma with enhanced CT: a retrospective study.

Frontiers in oncology·2026
Same author

Assessing Image Quality and Diagnostic Performance of Quadruple-Low Coronary CT Angiography with Deep Learning in High-BMI Patients.

Academic radiology·2026
Same author

An Adaptive Fusion Network for Breast Tumor Grading Based on Graph Structure Learning.

IEEE journal of biomedical and health informatics·2026

相关实验视频

Updated: Jun 27, 2026

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

2.6K

一个频率关注嵌入式网络用于聚片细分.

Rui Tang1, Hejing Zhao2,3, Yao Tong4,5

  • 1Department of Orthopedics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, China.

Scientific reports
|February 10, 2025
PubMed
概括
此摘要是机器生成的。

一种新的方法,频率注意嵌入式网络 (FAENet),在内镜图像中显著改善了胃肠片细分. 这种人工智能方法增强了边界和结构划分,以更好地检测和治疗多重体.

关键词:
聚片细分的细分方法这就是U-Net.注意力机制注意力机制

更多相关视频

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.4K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

353

相关实验视频

Last Updated: Jun 27, 2026

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

2.6K
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.4K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

353

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 胃肠病学 胃肠病学

背景情况:

  • 内镜成像对于观察和治疗胃肠多瘤至关重要.
  • 由于复杂的结构和多样化的组织环境,在内镜图像中精确细分聚是具有挑战性的.
  • 现有的细分方法在精确的多重体划分方面存在困难.

研究的目的:

  • 引入一种新的深度学习模型,即频率注意嵌入式网络 (FAENet),用于在内镜图像中增强聚细分.
  • 为了利用基于频率的注意力机制,提高细分胃肠道息肉的准确性.
  • 解决现有方法在区分聚与周围的粘膜组织方面的局限性.

主要方法:

  • 提出FAENet,一个利用基于频率的注意力机制的网络.
  • 将图像数据分离和处理成高频和低频组件.
  • 整合内部组件和跨组件的注意力,以完善聚边界和内部结构划分.

主要成果:

  • 在Kvasir-SEG和CVC-ClinicDB数据集上,FAENet在最先进的模型上表现优越.
  • 在子系数,对欧盟交叉点 (IoU),灵敏度和特异性方面观察到显著的改进.
  • 该方法有效地保留了边缘细节,并改进了学习的表示,以便进行强大的细分.

结论:

  • FAENet的先进的注意力机制在内镜成像中大大提高了多片细分质量.
  • 拟议的模型优于传统和当代的细分技术.
  • 通过改进细分,FAENet有可能彻底改变胃肠的临床诊断和治疗.