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

Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...

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

Predictive analytics in gamified education: A hybrid model for identifying at-risk students.

MethodsX·2025
Same author

Remote photoplethysmography-based human vital sign prediction using cyclical algorithm.

Journal of biophotonics·2023
Same journal

Kolmogorov-Arnold Guided Local-Global Attention for Medical Image Classification.

Journal of imaging informatics in medicine·2026
Same journal

Artificial Intelligence-Assisted Inner Ear Computed Tomography Analysis: Radiomics-Based Comparison of Affected and Unaffected Ears in Idiopathic Sudden Sensorineural Hearing Loss.

Journal of imaging informatics in medicine·2026
Same journal

High Adoption, Higher Expectations: A Cross-Sectional Survey of Radiologist Engagement with Artificial Intelligence in the United Arab Emirates.

Journal of imaging informatics in medicine·2026
Same journal

Complex-valued Multi-scale Hybrid Attention Network for Fast MRI via Sparsified Data Learning.

Journal of imaging informatics in medicine·2026
Same journal

Automatic Phase and Sequence Identification in Gd-EOB-DTPA-Enhanced Liver MRI Using Deep Convolutional and Sequential Learning.

Journal of imaging informatics in medicine·2026
Same journal

Ultrasound-Based AI in Predicting Hormone Receptor Status in Breast Cancer: Is "Digital Biopsy" Possible.

Journal of imaging informatics in medicine·2026
查看所有相关文章

相关实验视频

Updated: May 21, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.4K

生物医学图像分类的深度学习方法

Riddhi Virendra Doshi1, Sagarkumar S Badhiye2, Latika Pinjarkar2

  • 1Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India. riddhidoshi1996@gmail.com.

Journal of imaging informatics in medicine
|July 8, 2025
PubMed
概括
此摘要是机器生成的。

深度学习技术显著提升生物医学图像分类,以改善诊断. 这篇评论探讨了50种方法,突出了AI.

关键词:
生物医学图像 生物医学图像卷积神经网络 (CNN) 是一种神经网络.深度学习是一种深度学习.医疗保健 医疗保健 医疗保健 医疗保健转移学习转移学习

更多相关视频

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.1K
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

2.9K

相关实验视频

Last Updated: May 21, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.4K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.1K
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

2.9K

科学领域:

  • 医学成像和人工智能 医学成像和人工智能
  • 计算机辅助诊断 计算机辅助诊断
  • 医疗保健中的机器学习

背景情况:

  • 生物医学图像分类对于准确的医学诊断和患者结果至关重要.
  • 深度学习已经彻底改变了医学图像分析,为分类任务提供了强大的工具.
  • 多种不同的医学成像模式,如乳房影像,组织病理学和放射学,都受益于人工智能.

研究的目的:

  • 为生物医学图像分类中的深度学习应用提供全面的概述.
  • 讨论各种深度学习架构 (CNN,RNN,GAN) 和学习方法 (监督,无监督,强化学习).
  • 审查50种在医疗保健中用于疾病检测和图像细分等任务的深度学习方法.

主要方法:

  • 对生物医学成像中的50种深度学习方法的系统审查和分析.
  • 讨论深度学习架构:卷积神经网络 (CNN),循环神经网络 (RNN),生成对抗网络 (GAN).
  • 学习方法的分类:医疗图像分析中的监督,无监督和强化学习.

主要成果:

  • 深度学习模型在疾病检测,图像细分和各种医疗图像的分类方面表现出显著的有效性.
  • 强调在公开可用的数据集上训练的模型,展示开放访问数据对人工智能驱动的医疗保健创新的影响.
  • 确定50种在医疗保健领域应用的独特深度学习方法.

结论:

  • 深度学习为推进生物医学图像分析和诊断精度提供了变革性的潜力.
  • 使用公开可用的数据集加速了医疗保健人工智能的进展.
  • 未来的研究应该继续探索新的深度学习方法来应对复杂的生物医学成像挑战.