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Peter Moss Acute Myeloid & Lymphoblastic Leukemia AI Research Project

Acute Lymphoblastic Leukemia Classifiers 2019

Peter Moss Acute Myeloid & Lymphoblastic Leukemia AI Research Project

CURRENT RELEASE UPCOMING RELEASE Issues Welcome! Issues LICENSE

 

Table Of Contents

 

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.

Projects Description Status Author
Data Augmentation Applies filters to datasets and increases the amount of training / test data. Complete Adam Milton-Barker
AllCNN Caffe Classifier Acute Lymphoblastic Leukemia classifier created using the Caffe framework. Ongoing Adam Milton-Barker
Movidius NCS Classifier Acute Lymphoblastic Leukemia classifier created using the Intel Movidius NCS. Complete Adam Milton-Barker
FastAI Resnet50 Classifier Acute Lymphoblastic Leukemia classifier created using FastAI & Resnet50. Complete Salvatore Raieli
FastAI Resnet50(a) Classifier Acute Lymphoblastic Leukemia classifier created using FastAI & Resnet50. Complete Adam Milton-Barker
FastAI Resnet34 Classifier Acute Lymphoblastic Leukemia classifier created using FastAI & Resnet34. Complete Salvatore Raieli
FastAI Resnet18 Classifier Acute Lymphoblastic Leukemia classifier created using FastAI & Resnet18. Complete Salvatore Raieli
QuantisedCode Acute Lymphoblastic Leukemia classifier created using Keras with Tensorflow Backend, Paper 1 and the original Dataset 2. Complete Dr Amita Kapoor & Taru Jain
AllCNN Acute Lymphoblastic Leukemia classifier created using Keras with Tensorflow Backend, Paper 1 and the original Dataset 1. Complete Adam Milton-Barker
AllCNN Acute Lymphoblastic Leukemia classifier created using Keras with Tensorflow Backend, Paper 1 and the original Dataset 2. Ongoing Adam Milton-Barker

 

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.

 

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

Students Contributors

 

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.