Peter Moss Acute Myeloid & Lymphoblastic Leukemia AI Research Project

Acute Lymphoblastic Leukemia Classifiers 2019

Table Of Contents

• Introduction
• DISCLAIMER
• Projects
• Data Augmentation
• Contributing
  • Contributors
  • Student Contributors
• Versioning
• License
• Bugs/Issues

Introduction

The Peter Moss Acute Lymphoblastic Leukemia classifiers are a collection of projects that use computer vision to classify Acute Lymphoblastic Leukemia (ALL) in unseen images.

This repository includes classifier projects made with Tensorflow, Caffe, Keras, FastAI & Intel Movidius (NCS).

DISCLAIMER

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.

Developers that have contributed to this repository have experience in using Artificial Intelligence for detecting certain types of cancer. They are not a doctors, medical or cancer experts.

Salvatore Raieli is a bioinformatician researcher and PhD in Immunology, but does not work in medical diagnosis.

Dr Amita Kapoor is Associate Professor at SRCASW, University of Delhi, and teaches Neural Networks, Artificial Intelligence, Operating system, Embedded system, Computer Communication and Networking.

Please use these systems responsibly.

Projects

This repository hosts a collection of classifiers that have been developed by the team using the Python programming language. These classifiers include Caffe, FastAI, Movidius NCS1 and Keras classifiers, each project may have multiple classifiers.

Team Publications

A series of articles / tutorials by Adam Milton-Barker that take you through attempting to replicate the work carried out in the Acute Myeloid Leukemia Classification Using Convolution Neural Network In Clinical Decision Support System paper.

• Acute Lymphoblastic Leukemia Data Augmentation (Intel® AI Developer Program) - Adam Milton-Barker

• Inception V3 Deep Convolutional Architecture For Classifying Acute Myeloid/Lymphoblastic Leukemia (Intel® AI Developer Program) - Adam Milton-Barker

• Introduction to convolutional neural networks in Caffe* - Adam Milton-Barker

• Preparing the Acute Lymphoblastic Leukemia dataset - Adam Milton-Barker

Contributing

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.

Contributors

• Adam Milton-Barker - Asociacion De Investigation En Inteligencia Artificial Para La Leucemia Peter Moss President & Lead Developer, Sabadell, Spain

• Salvatore Raieli - Asociacion De Investigation En Inteligencia Artificial Para La Leucemia Peter Moss Bioinformatics & Immunology AI R&D, Bologna, Italy

• Dr Amita Kapoor - Asociacion De Investigation En Inteligencia Artificial Para La Leucemia Peter Moss Student Program Team / R&D, Delhi, India

Students Contributors

• Taru Jain - Asociacion De Investigation En Inteligencia Artificial Para La Leucemia Peter Moss Student Program, Delhi, India

Versioning

We use SemVer for versioning. For the versions available.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Bugs/Issues

We use the repo issues to track bugs and general requests related to using this project.