Since our first research project began, we have been dedicated to finding and sharing open information about Leukemia, as well as datasets, code and research papers.
The Asociación de Investigacion en Inteligencia Artificial Para la Leucemia Peter Moss Information Database is a collection of open information related to Leukemia, other blood/bone marrow cancers & COVID-19.
This database began in 2018 as a Github repository.
We welcome information contributions from the public. If you would like to share something that you feel is relevant for this directory, please choose "Submit Information" in the form on our contact page.
Research Centers / Hospitals & Universities
In this database you will find an archive of research centers, hospitals & universities working on the global issue of Leukemia, other blood/bone marrow cancers & COVID-19.
Open Research Papers
In this database you will find an archive of leukemia, other blood/bone marrow cancer & COVID-19 related research papers.
Open-Source Code
In this database you will find an archive of open-source code that can be used for medical research such as Leukemia, other blood/bone marrow cancers & COVID-19 detection.
Open Datasets
In this database you will find an archive of open leukemia, other blood/bone cancer & COVID-19 related datasets.
Leukemia
In this database you will find an archive of open leukemia information.
Bone Marrow Cancers
In this database you will find an archive of open information about Bone Marrow Cancers.
Blood Diseases
In this database you will find an archive of open information about Blood Diseases.
COVID-19 (SARS-CoV-2)
In this database you will find an archive of open information about COVID-19 (SARS-CoV-2).
Drugs
In this database you will find an archive of information about drugs for leukemia, other blood/bone marrow cancers & COVID-19.
Families & Carers
In this database you will find an archive of information for families and carers dealing with leukemia, other blood/bone cancers & COVID-19 .
Acute Myeloid Leukemia: A general introduction
In this article our immunology and bioinformatics expert Salvatore Raieli, focuses on machine learning and deep learning in medical images diagnosis. The increase in available data, hardware capabilities and cloud computing are allowing a great development in the field, and medicine is benefiting from this revolution. Nowadays, many algorithms can be run in a personal computer or in cloud service, increasing the potential number of users and researchers.Find Out More
Mayo Clinic, Rochester, Minnesota, USA
The Mayo Clinic is an American nonprofit academic medical center based in Rochester, Minnesota, focused on integrated clinical practice, education, and research. It employs more than 4,500 physicians and scientists, along with another 58,400 administrative and allied health staff.Find Out More
SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification
In this paper, we build a public available SARS-CoV-2 CT scan dataset, containing 1252 CT scans that are positive for SARS-CoV-2 infection (COVID-19) and 1230 CT scans for patients non-infected by SARS-CoV-2, 2482 CT scans in total. These data have been collected from real patients in hospitals from Sao Paulo, Brazil. Authors: Plamen Angelov, Eduardo SoaresFind Out More
COVID-19 AI Data Analysis
This repository will provide several projects created by our data scientists. Projects will include installation scripts, documentation and code for AI algorithms built for understanding COVID-19.Find Out More
ALL FastAI Resnet 18 Classifier
The ALL FastAI Resnet 18 Classifier was created by Adam Milton-Barker based on Salvatore Raieli's Resnet50 project. The classifier provides a Google Colab notebook that uses FastAI with Resnet18 and ALL_IDB2 from the Acute Lymphoblastic Leukemia Image Database for Image Processing dataset.Find Out More
Bonn Datasets of meta-analysis on AML classification
Bonn Datasets of meta-analysis on AML classificationFind Out More
Acute Lymphoblastic Leukemia Papers Evaluation Part 1 Tensorflow 2.0
Here we will replicate the network architecture and data split proposed in the Acute Leukemia Classification Using Convolution Neural Network In Clinical Decision Support System paper and compare our results. In the paper, the authors do not go into the evaluation of the model, however, in this project we will go deeper into how well the model actually does. Author: Adam Milton-Barker.Find Out More
Scalable Prediction of Acute Myeloid Leukemia Using High-Dimensional Machine Learning and Blood Transcriptomics
Our results support the notion that transcriptomics combined with machine learning could be used as part of an integrated -omics approach wherein risk prediction, differential diagnosis, and subclassification of AML are achieved by genomics while diagnosis could be assisted by transcriptomic-based machine learning. AUTHORS: Stefanie Warnat-Herresthal Konstantinos Perrakis, Bernd Taschler, Matthias Becker, Kevin Baßler, Marc Beyer, Patrick Gu¨ nther,1 Jonas Schulte-Schrepping, Lea Seep, Kathrin Klee, Thomas Ulas, Torsten Haferlach, Sach Mukherjee, and Joachim L. SchultzeFind Out More
Acute Lymphoblastic Detection System 2019 Data Augmentation
The ALL Detection System 2019 Data Augmentation program applies augmentations/filters to datasets and increases the amount of training/test data available to use. The program is part of the computer vision research and development for the Peter Moss Acute Myeloid & Lymphoblastic Leukemia AI Research Project.Find Out More
All-IDB: The acute lymphoblastic leukemia image database for image processing
The visual analysis of peripheral blood samples is an important test in the procedures for the diagnosis of leukemia.Find Out More