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深度EM操场:将深度学习带到电子显微镜实验室

Hannah Kniesel¹, Poonam Poonam¹, Tristan Payer¹

¹Visual Computing Group, Ulm University, Ulm, Germany.

Journal of microscopy

|June 28, 2025

概括

深度EM游戏场使得电子显微镜 (EM) 实验室可以使用深度学习 (DL). 这个平台使EM研究人员能够培训和应用DL模型,弥合AI研究和实践实验室使用之间的差距.

科学领域:

电子显微镜电子显微镜
人工智能的人工智能
图像分析图像分析

背景情况:

深度学习 (DL) 已经彻底改变了图像分析,但其在电子显微镜 (EM) 实验室的采用受到可访问性和专业知识障碍的阻碍.
电子商务专家往往缺乏实施和解释AI模型所需的编码技能和深度学习知识.
在先进的DL研究与其在常规EM工作流程中的实际应用之间存在差距.

研究的目的:

介绍DeepEM Playground,这是一个旨在民主化电子显微镜深度学习的交互平台.
授权EM研究人员,无论他们的编码背景如何,培训,调整和应用DL模型.
促进EM实验室内更深入地理解和整合人工智能驱动的图像分析.

主要方法:

开发一个交互式,用户友好的平台,DeepEM游乐场.
为DL模型培训和EM应用提供指导,实践的方法.
专注于在EM专家中降低DL采用的入门障碍.

主要成果:

深度EM游戏场使EM研究人员能够在没有广泛的编码专业知识的情况下参与DL方法.
该平台支持DL模型的初始探索和高级定制,用于EM图像分析.
促进了人工智能在EM工作流程中更有信心和更有效的整合.

结论:

深度EM游乐场成功地弥合了DL研究和EM实验室实践之间的差距.
该平台促进了对电子显微镜中人工智能驱动的分析的更大理解和采用.
授权EM社区利用先进的DL工具进行增强的图像分析和发现.

关键词:

计算机视觉计算机视觉深度学习是一种深度学习.电子显微镜的电子显微镜

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