AdamMiltonBarker's Projects
An Acute Lymphoblastic Leukemia classifier trained using Intel Distribution for Python and Intel Optimized Tensorflow (Tensorflow 2.1), and using OpenVINO to deploy the model on UP2 & Raspberry Pi.
A Tensorflow 2 project based on the research outlined in Human-level recognition of blast cells in the Acute myeloid leukemia with convolutional neural networks paper by Christian Matek, Simone Schwarz, Karsten Spiekermann , and Carsten Marr.
Open source Artificial Intelligence for COVID-19 detection/early detection. Includes Convolutional Neural Networks (CNN) & Generative Adversarial Networks (GAN)
Open-source Artificial Intelligence & Data Analysis research and development. AI algorithms built for fighting and understanding COVID-19.
A Knowledge Base for the FB Group Artificial Intelligence and Deep Learning (AIDL)
An Acute Lymphoblastic Leukemia classifier developed for the NVIDIA AGX Xavier using Intel® oneAPI AI Analytics Toolkit, Intel® Optimization for Tensorflow*, and TensorRT.
The ALL Arduino Nano 33 BLE Sense Classifier is an experiment to explore how low powered microcontrollers, specifically the Arduino Nano 33 BLE Sense, can be used to detect Acute Lymphoblastic Leukemia.
A series of Acute Lymphoblastic Leukemia CNNs trained with the C-NMC Dataset by University of Arkansas for Medical Sciences (UAMS). The projects are programmed in Python using various frameworks.
A collection of computer vision projects for Acute Lymphoblastic Leukemia classification/early detection.
The Peter Moss Acute Myeloid & Lymphoblastic Leukemia Detection System is a range of opensource classifiers with a locally hosted, database driven UI for data management, training, and running inference on Convolutional Neural Networks on the edge.
Acute Lymphoblastic Leukemia Detection System 2020 uses Tensorflow 2 & Oculus Rift to provide a virtual diagnosis system. Project by Adam Milton-Barker.
Combines Oculus Rift's Virtual Reality technologies with a Deep Learning Classifier to provide real-time classification of Acute Lymphoblastic Leukemia in peripheral blood samples within a Virtual Reality environment.
A series of Acute Lymphoblastic Leukemia CNNs programmed in Python using FastAI. Project by team member Salvatore Raieli.
An Acute Lymphoblastic Leukemia GAN programmed in Python using Keras. Project by research intern Taru Jain, and Amita Kapoor.
Classifiers created with various languages and frameworks, using Fabio Scotti's ALL-IDB (Acute Lymphoblastic Leukemia Image Database for Image Processing) dataset.
An Acute Lymphoblastic Leukemia classifier developed for the NVIDIA Jetson Nano. Jetson AI Certification project by Adam Milton-Barker.
An Acute Lymphoblastic Leukemia classifier programmed in Python using Keras, by research intern Taru Jain, and Amita Kapoor.
An Acute Lymphoblastic Leukemia CNN programmed in Python using PyTorch. Project by Adam Milton-Barker.
An Acute Lymphoblastic Leukemia classifier developed using the Pytorch framework.
A classifier for Acute Lymphoblastic Leukemia developed using the R programming language.
An Acute Lymphoblastic Leukemia CNN based on the proposed architecture in the Acute Leukemia Classification Using Convolution Neural Network In Clinical Decision Support System paper, using the Acute Lymphoblastic Leukemia Image Database for Image Processing dataset.
Acute Lymphoblastic Leukemia classification using segmentation and the Acute Lymphoblastic Leukemia Image Database for Image Processing dataset. Programmed in Python using Tensrflow.
Open-source drug discovery for Acute Myeloid & Acute Lymphoblastic Leukemia.
The Peter Moss Acute Myeloid/Lymphoblastic Leukemia Project Research Archives is a place dedicated to sharing the public information, papers, code and datasets that we come across through R&D.
An Acute Myeloid Leukemia diagnostics project based on the research outlined in Scalable Prediction of Acute Myeloid Leukemia Using High-Dimensional Machine Learning and Blood Transcriptomics by Stefanie WarnatHerresthal, Konstantinos Perrakis, Bernd Taschler, Torsten Haferlach, Sach Mukherjee & Joachim L. Schultze.
A public repo of open Acute Myeloid Leukemia research papers discovered during project R&D
The contributing guide provides instructions on how to contribute to the Peter Moss Acute Myeloid and Lymphoblastic Leukemia AI Research Project.
A repository containing all project type specific contributing guides for project contributors to follow.
Artificial Intelligence & Quantum processing research and development. AI algorithm training & running on DWave Leap, aimed at fighting, and understanding COVID-19.
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