Journal article 920 views 577 downloads
Deepometry, a framework for applying supervised and weakly supervised deep learning to imaging cytometry
Minh Doan,
Claire Barnes 
,
Claire McQuin,
Juan C. Caicedo,
Allen Goodman,
Anne E. Carpenter,
Paul Rees
,
Claire McQuin,
Juan C. Caicedo,
Allen Goodman,
Anne E. Carpenter,
Paul Rees
Nature Protocols, Volume: 16, Issue: 7, Pages: 3572 - 3595
Swansea University Authors:
Claire Barnes , Paul Rees
-
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DOI (Published version): 10.1038/s41596-021-00549-7
Abstract
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 ex...
| Published in: | Nature Protocols |
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| ISSN: | 1754-2189 1750-2799 |
| Published: |
Springer Science and Business Media LLC
2021
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa57174 |
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| Abstract: |
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. |
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| College: |
Faculty of Science and Engineering |
| Issue: |
7 |
| Start Page: |
3572 |
| End Page: |
3595 |