From Pixels to Information: Artificial Intelligence in Fluorescence Microscopy

IF 3.7 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY Advanced Photonics Research Pub Date : 2024-07-15 DOI:10.1002/adpr.202300308
Seungjae Han, Joshua Yedam You, Minho Eom, Sungjin Ahn, Eun-Seo Cho, Young-Gyu Yoon
{"title":"From Pixels to Information: Artificial Intelligence in Fluorescence Microscopy","authors":"Seungjae Han,&nbsp;Joshua Yedam You,&nbsp;Minho Eom,&nbsp;Sungjin Ahn,&nbsp;Eun-Seo Cho,&nbsp;Young-Gyu Yoon","doi":"10.1002/adpr.202300308","DOIUrl":null,"url":null,"abstract":"<p>This review explores how artificial intelligence (AI) is transforming fluorescence microscopy, providing an overview of its fundamental principles and recent advancements. The roles of AI in improving image quality and introducing new imaging modalities are discussed, offering a comprehensive perspective on these changes. Additionally, a unified framework is introduced for comprehending AI-driven microscopy methodologies and categorizing them into linear inverse problem-solving, denoising, and nonlinear prediction. Furthermore, the potential of self-supervised learning techniques that address the challenges associated with training the networks are explored, utilizing unlabeled microscopy data to enhance data quality and expand imaging capabilities. It is worth noting that while the specific examples and advancements discussed in this review focus on fluorescence microscopy, the general approaches and theories are directly applicable to other optical microscopy methods.</p>","PeriodicalId":7263,"journal":{"name":"Advanced Photonics Research","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adpr.202300308","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Photonics Research","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/adpr.202300308","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

This review explores how artificial intelligence (AI) is transforming fluorescence microscopy, providing an overview of its fundamental principles and recent advancements. The roles of AI in improving image quality and introducing new imaging modalities are discussed, offering a comprehensive perspective on these changes. Additionally, a unified framework is introduced for comprehending AI-driven microscopy methodologies and categorizing them into linear inverse problem-solving, denoising, and nonlinear prediction. Furthermore, the potential of self-supervised learning techniques that address the challenges associated with training the networks are explored, utilizing unlabeled microscopy data to enhance data quality and expand imaging capabilities. It is worth noting that while the specific examples and advancements discussed in this review focus on fluorescence microscopy, the general approaches and theories are directly applicable to other optical microscopy methods.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
从像素到信息:荧光显微镜中的人工智能
这篇综述探讨了人工智能(AI)如何改变荧光显微技术,概述了其基本原理和最新进展。文章讨论了人工智能在提高图像质量和引入新成像模式方面的作用,为这些变化提供了一个全面的视角。此外,还介绍了一个统一的框架,用于理解人工智能驱动的显微镜方法,并将其分为线性逆向问题解决、去噪和非线性预测。此外,还探讨了自监督学习技术的潜力,这些技术可以利用未标记的显微镜数据来提高数据质量并扩展成像能力,从而解决与训练网络相关的挑战。值得注意的是,虽然本综述中讨论的具体例子和进展侧重于荧光显微镜,但一般方法和理论可直接适用于其他光学显微镜方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
2.70%
发文量
0
期刊最新文献
Masthead Structural Colors Derived from the Combination of Core–Shell Particles with Cellulose Ultrafast Terahertz Superconductor Van der Waals Metamaterial Photonic Switch Masthead Progress on Coherent Perovskites Emitters: From Light-Emitting Diodes under High Current Density Operation to Laser Diodes
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1