Python(编程语言)
计算机科学
脚本语言
人工智能
深度学习
可视化
协议(科学)
机器学习
监督学习
推论
模式识别(心理学)
人工神经网络
医学
操作系统
病理
替代医学
作者
Minh Doan,Claire M. Barnes,Claire McQuin,Juan Carlos Caicedo,Allen Goodman,Anne E. Carpenter,Paul Rees
出处
期刊:Nature Protocols
[Springer Nature]
日期:2021-06-18
卷期号:16 (7): 3572-3595
被引量:26
标识
DOI:10.1038/s41596-021-00549-7
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI