| Table of Contents |
|---|
- Exploratory Data Analysis (EDA)
- fastai utils
- ML experiments
- performance assessment
Author(s)
| Name | Institution | Mail Address | Social Contacts | |
|---|---|---|---|---|
| Luca Clissa | INFN Bologna | luca.clissa@bo.infn.it | Skype: luca.clissa92; Linkedin: https://www.linkedin.com/in/luca-clissa-b3908695/; Medium: https://medium.com/@luca.clissa ; Hangouts: | Skype: ; Linkedin: ; Twitter: ; Hangouts: |
How to Obtain Support
| luca.clissa@bo.infn.it | |
| Social | Skype: ; Linkedin: ; Twitter: ; Hangouts: |
| Jira |
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| Programming Language | Python |
|---|---|
| ML Toolset | PyTorch, fastai |
| Additional libraries | skimage, opencv |
| Suggested Environments | see requirements and installation instructions |
Needed datasets
| Data CreatorCreators | Luca Clissa, et al. |
|---|---|
| Data Type | Real data; fluorescence microscopy images |
| Data Size | 414 MB |
| Data Source | http://amsacta.unibo.it/6706/ |
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Simply clone the repository https://github.com/clissa/object-counting-ML-INFN and follow the instructions in installation_instructions.txt to set up your workspace. Then download the data as described in the notebook 01. Exploratory Data Analysis.ipynb. Each step of the analysis is detailed in a dedicated notebook under the folder notebooks .
Annotated Description
- 01. Eploratory Data Analysis.ipynb: instructions for downloading the dataset and setting up the workspace; data exploration: formats, peculiarities and challenges
- 02. fastai building blocks.ipynb: quick start with fastai library; Dataloaders, Learners and training loop
- 03. Experiments - Dice loss.ipynb: model training and experiments
- 04. Results - Visual inspection.ipynb: performance assessment by visual inspection; qualitative evaluation
- 05. Results - Detection & counting performance.ipynb: performance assessment with detection and counting metrics; quantitative evaluation
References
Morelli, R., Clissa, L., Amici, R. et al. Automating cell counting in fluorescent microscopy through deep learning with c-ResUnet. Sci Rep 11, 22920 (2021).
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