- 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 |
General Information
ML/DL Technologies | CNN, ResUnet |
---|---|
Science Fields | Computer Vision; Microscopic Fluorescence; Semantic Segmentation; Object Detection; Object Counting |
Difficulty | medium |
Language | Italiano |
Type | fully annotated; runnable |
Software and Tools
Programming Language | Python |
---|---|
ML Toolset | PyTorch, fastai |
Additional libraries | skimage, opencv |
Suggested Environments | see requirements and installation instructions |
Needed datasets
Data Creator | Luca Clissa, et al. |
---|---|
Data Type | Real data; fluorescence microscopy images |
Data Size | 414 MB |
Data Source | http://amsacta.unibo.it/6706/ |
Short Description of the Use Case
Counting objects is a learning task common to many applications, from video sourveillance to agriculture 4.0, not to mention studies in life-sciences and medicine. However, this task is typically performed manually by domain experts, becoming very demanding in terms of time and human resources. Also, this increases the chances of errors due to distraction or fatigue.
This use-case deals with an approach to automate recognition and counting of objects in images. Specifically, we frame the problem as a *semantic segmentation* task and we use the c-Resunet network architecture, taking the Fluorescent Neuronal Cells dataset as a benchmark.
The material is organized into notebooks that cover every stage of a realistic data analysis pipeline. In particular, a great deal of attention is devoted to EDA both to expose the challenges of the dataset and to evaluate the results. Likewise, technical aspects of the fastai implementation are detailed.
How to execute it
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
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).
Clissa, L. Supporting Scientific Research Through Machine and Deep Learning: Fluorescence Microscopy and Operational Intelligence Use Cases. PhD Thesis (2022)
Clissa, L. et al. Fluorescent Neuronal Cells. AMS Acta (2021)
Fluorescent Neuronal Cells dataset – part I, TDS Blog
Fluorescent Neuronal Cells dataset – part II, TDS Blog
Fluorescent Neuronal Cells dataset – part III, TDS Blog