- Introduction
- DISCLAIMER
- Getting Started
- ALL-IDB
- FastAI Classifier Projects
- Contributing
- Versioning
- License
- Bugs/Issues
A series of Acute Lymphoblastic Leukemia CNNs programmed in Python using FastAI. Project by team member Salvatore Raieli.
These projects should be used for research purposes only. The purpose of the projects is to show the potential of Artificial Intelligence for medical support systems such as diagnosis systems.
Although the classifiers are accurate and show good results both on paper and in real world testing, they are not meant to be an alternative to professional medical diagnosis.
Salvatore Raieli is a bioinformatician researcher and PhD in Immunology, but does not work in medical diagnosis. Please use these systems responsibly.
Please use this system responsibly.
To get started there are some things you need to collect:
These tutorials were run on Google Colab.
In this project I used Python 3, FastAI.
Clone the ALL FastAI 2020 repository from the Peter Moss Acute Myeloid & Lymphoblastic Leukemia AI Research Project Github Organization.
To clone the repository and install the ALL FastAI 2020 classifiers, make sure you have Git installed. Now navigate to the home directory on your device using terminal/commandline, and then use the following command.
$ git clone https://github.com/AMLResearchProject/ALL-FastAI-2020.git
Once you have used the command above you will see a directory called ALL-FastAI-2020 in your home directory.
ls
Using the ls command in your home directory should show you the following.
ALL-FastAI-2020
Navigate to ALL-FastAI-2020/Projects/ directory, this is your project root directory for this tutorial.
Developers from the Github community that would like to contribute to the development of this project should first create a fork, and clone that repository. For detailed information please view the CONTRIBUTING guide. You should pull the latest code from the development branch.
$ git clone -b "0.1.0" https://github.com/AMLResearchProject/ALL-FastAI-2020.git
The -b "0.1.0" parameter ensures you get the code from the latest master branch. Before using the below command please check our latest master branch in the button at the top of the project README.
You need to be granted access to use the Acute Lymphoblastic Leukemia Image Database for Image Processing dataset. You can find the application form and information about getting access to the dataset on this page as well as information on how to contribute back to the project here. If you are not able to obtain a copy of the dataset please feel free to try this tutorial on your own dataset, we would be very happy to find additional AML & ALL datasets.
In this project, ALL-IDB1 is used, one of the datsets from the Acute Lymphoblastic Leukemia Image Database for Image Processing dataset. We will use data augmentation to increase the amount of training and testing data we have.
"The ALL_IDB1 version 1.0 can be used both for testing segmentation capability of algorithms, as well as the classification systems and image preprocessing methods. This dataset is composed of 108 images collected during September, 2005. It contains about 39000 blood elements, where the lymphocytes has been labeled by expert oncologists. The images are taken with different magnifications of the microscope ranging from 300 to 500."
| Model | Project | Description | Status | Author |
|---|---|---|---|---|
| Resnet | FastAI Resnet18 Classifier | A FastAI model trained using Resnet18 | Complete | Salvatore Raieli |
| Resnet | FastAI Resnet34 Classifier | A FastAI model trained using Resnet34 | Complete | Salvatore Raieli |
| Resnet | FastAI Resnet50 Classifier | A FastAI model trained using Resnet50 | Complete | Salvatore Raieli |
| Resnet | FastAI Resnet101 Classifier | A FastAI model trained using Resnet101 | Complete | Salvatore Raieli |
| Resnet | FastAI Resnet152 Classifier | A FastAI model trained using Resnet152 | Complete | Salvatore Raieli |
| AlexNet | FastAI AlexNet Classifier | A FastAI model trained using AlexNet | Complete | Salvatore Raieli |
| Densenet | FastAI Densenet121 Classifier | A FastAI model trained using Densenet121 | Complete | Salvatore Raieli |
| Densenet | FastAI Densenet161 Classifier | A FastAI model trained using Densenet161 | Complete | Salvatore Raieli |
| Densenet | FastAI Densenet169 Classifier | A FastAI model trained using Densenet169 | Complete | Salvatore Raieli |
| Densenet | FastAI Densenet201 Classifier | A FastAI model trained using Densenet201 | Complete | Salvatore Raieli |
| SqueezeNet | FastAI SqueezeNet_1_0 Classifier | A FastAI model trained using SqueezeNet_1_0 | Complete | Salvatore Raieli |
| SqueezeNet | FastAI SqueezeNet_1_1 Classifier | A FastAI model trained using SqueezeNet_1_1 | Complete | Salvatore Raieli |
| VGG | FastAI VGG16 Classifier | A FastAI model trained using VGG16 | Complete | Salvatore Raieli |
| VGG | FastAI VGG19 Classifier | A FastAI model trained using VGG19 | Complete | Salvatore Raieli |
The Peter Moss Acute Myeloid & Lymphoblastic Leukemia AI Research project encourages and welcomes code contributions, bug fixes and enhancements from the Github.
Please read the CONTRIBUTING document for a full guide to forking our repositories and submitting your pull requests. You will also find information about our code of conduct on this page.
- Salvatore Raieli - Asociacion De Investigation En Inteligencia Artificial Para La Leucemia Peter Moss Bioinformatics & Immunology AI R&D, Bologna, Italy
We use SemVer for versioning.
This project is licensed under the MIT License - see the LICENSE file for details.
We use the repo issues to track bugs and general requests related to using this project. See CONTRIBUTING for more info on how to submit bugs, feature requests and proposals.