.png){kind=icon}
返回
Deepometry, a framework for applying supervised and weakly supervised deep learning to imaging cytometry
.png){kind=icon}
OA
AI
DOI：10.1038/s41596-021-00549-7.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This protocol describes Deepometry, an open-source application for supervised and weakly supervised deep learning analysis of imaging flow cytometry datasets. The protocol provides runtime scripts for Python, MATLAB and a standalone application. Deep learning offers the potential to extract more than meets the eye from images captured by imaging flow cytometry. This protocol describes the application of deep learning to single-cell images to perform supervised cell classification and weakly supervised learning, using example data from an experiment exploring red blood cell morphology. We describe how to acquire and transform suitable input data as well as the steps required for deep learning training and inference using an open-source web-based application. All steps of the protocol are provided as open-source Python as well as MATLAB runtime scripts, through both command-line and graphic user interfaces. The protocol enables a flexible and friendly environment for morphological phenotyping using supervised and weakly supervised learning and the subsequent exploration of the deep learning features using multi-dimensional visualization tools. The protocol requires 40 h when training from scratch and 1 h when using a pre-trained model.
Keyword:
FLOW-CYTOMETRY
CELL
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
16
论文数:
4.0K
被引数:
5.6W
机构
引用论文
An open-source solution for advanced imaging flow cytometry data analysis using machine learning.png)
METHODSOAAI
METHODSIF4.3
OAAI分享收藏分享收藏分享收藏Structural study of lanthanides(III) in aqueous nitrate and chloride solutions by EXAFS通过EXAFS对硝酸盐和氯化物水溶液中镧系元素 (III) 的结构研究
PREAI
分享收藏Reconstructing cell cycle and disease progression using deep learning使用深度学习重建细胞周期和疾病进展NATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏
查看更多内容