The Peter Moss Acute Myeloid & Lymphoblastic Leukemia AI Research Project focuses on the use of Artificial Intelligence for detetion of Acute Myeloid Leukemia (AML) and Lymphoblastic Leukemia (ALL). Early detection is an unsolved problem that researchers continue to work on around the globe. We hope to use Artificial Intelligence to find a way of solving the problem of early detection.
The research project is made up of a number development projects, each using different frameworks or programming languages. All of the development projects are open-source and can be found on the official Github.
The Peter Moss Acute Myeloid & Lymphoblastic Leukemia AI Research Project was created after Peter was diagnosed with Acute Myeloid Leukemia and given a few weeks to live, the diagnosis came one month after an all clear blood test. Peter's grandson Adam Milton-Barker was convinced there must have been signs in that blood test that gave indication of the leukemia.
A Facebook post in AIDL group on Facebook brought the project to the attention of Ho Leung Ng, Associate Professor of Biochemistry & Molecular Biophysics at Kansas State University. As a leukemia researcher, Ho was able to share information of datasets, research papers and access to people who are researching/volunteering their time/experience to early detection and drug discovery for Acute Myeloid and Lymphoblastic Leukemia.
One of the primary focuses of the Peter Moss Acute Myeloid & Lymphoblastic Leukemia AI Research Project is to find and publish information that may be useful for families that need support, students, developers and researchers in the medical field. We release all public information relating to Acute Myeloid & Lymphoblastic Leukemia that we come accross to the Asociación de Investigacion en Inteligencia Artificial Para la Leucemia Peter Moss Open Information Database. The database began as a Github repository, before being made into a free online service when we opened the Association in 2020.
Information that we publish includes links to Acute Myeloid & Lymphoblastic Leukemia related information, papers, datasets, cancer/leukemia research centers, drugs and other AML/ALL related public information we come across during life experience and our research.
Adam Milton-Barker
Intel AI Dev Program Documentation
The AML/ALL Classifier Data Augmentation program applies filters to datasets and increases the amount of training / test data available to use.
READ MOREAdam Milton-Barker
Intel AI Dev Program Documentation
In this article I will cover the steps required to create the dataset required to train the model using the network we defined in the previous tutorial. The article will cover the paper exactly, and will use the original 108 image dataset (ALL_IDB1).
READ MOREAdam Milton-Barker
Intel AI Dev Program Documentation
This is the first part of a series of articles that will take you through my experience building a custom classifier with Caffe that should be able to detect Acute Lymphoblastic Leukemia.
READ MOREIntel Developer Zone
At the Intel demo showcase at Embedded World*, members of a broad Intel ecosystem showcased solutions enabled by Intel® IoT hardware technologies and software in four main areas: computer vision, edge AI, secure provisioning, and workload consolidation.
READ MOREAdam Milton-Barker
Intel AI Dev Program Documentation
This article will take you through some information about Inception V3, transfer learning, and how we use these tools in the Acute Myeloid/Lymphoblastic Leukemia AI Research Project.
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The Peter Moss Acute Myeloid/Lymphoblastic Leukemia Detection System is a featured project on Intel Devmesh, the Intel Developer Zone social network.
The project is an open source extension of the GeniSys AI Artificial Intelligence Network that allows you to upload AML/ALL test data and run classifications to detect positive and negative examples using Intel technologies.
The project has recently been voted 1st in Europe out of 4 projects, and awarded the Intel® DevMesh AI Spotlight Award which is a new designation granted by Intel recognizing inspiring and breakthrough Artificial Intelligence projects in development from the Intel software community.