https://github.com/czbiohub-sf/label-free-malaria
Image processing software for the Label-free malaria imaging project.
Science Score: 23.0%
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○CITATION.cff file
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○codemeta.json file
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✓Academic publication links
Links to: plos.org -
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Low similarity (6.4%) to scientific vocabulary
Last synced: 6 months ago
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Repository
Image processing software for the Label-free malaria imaging project.
Basic Info
- Host: GitHub
- Owner: czbiohub-sf
- License: bsd-3-clause
- Language: MATLAB
- Default Branch: master
- Size: 314 KB
Statistics
- Stars: 1
- Watchers: 6
- Forks: 0
- Open Issues: 0
- Releases: 0
Created over 5 years ago
· Last pushed almost 3 years ago
https://github.com/czbiohub-sf/Label-Free-Malaria/blob/master/
# Label-Free-Malaria Image processing software for the Label-free malaria imaging project. ## Introduction This repo contains Matlab code for the label-free malaria imaging project. In particular, an image analysis pipeline is implemented in order to segment and classify red blood cell images into four categories: [healthy, ring, trophozoite, and schizont]. The last three categories are three blood stages of the parasite we trained our networks to recognize. The pipeline was created in order to train and validate image classifiers at multiple wavelengths and focal slices in order to evaluate performance as a function of these variables. The pipeline allows for direct comparison, ensuring that the same physical RBCs are aligned and labeled identically for all conditions prior to training and validation. A link to our published manuscript: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009257 ## How it works  ## Requirements - Matlab 2019b+ - Computer Vision Toolbox - Deep Learning Toolbox - Deep Learning Toolbox Model for selected network (Only GoogLeNet has been implemented)
Owner
- Name: Chan Zuckerberg Biohub San Francisco
- Login: czbiohub-sf
- Kind: organization
- Location: San Francisco
- Website: https://www.czbiohub.org/sf/
- Repositories: 1
- Profile: https://github.com/czbiohub-sf