This blog category focuses on the research data we come across and/or use during our research.

COVID 2020 — A data scientist perspective 2020-03-18

COVID 2020 — A data scientist perspective

Team member Dr Amita Kapoor published her findings on the COVID-19 pandemic. This research was referenced in the peer-reviewed paper: Covid-19 spread: Reproduction of data and prediction using a SIR model on Euclidean network by Kathakali Biswas, Abdul Khaleque, and Parongama Sen.

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Detecting Acute Lymphoblastic Leukemia Using Caffe*, OpenVINO™ and Intel® Neural Compute Stick 2: Part 2 2018-03-18

Detecting Acute Lymphoblastic Leukemia Using Caffe*, OpenVINO™ and Intel® Neural Compute Stick 2: Part 2

In the first part of this series: Introduction to convolutional neural networks in Caffe*, I covered the steps to recreate the basics of the convolutional neural network proposed in the paper: Acute Myeloid Leukemia Classification Using Convolution Neural Network In Clinical Decision Support System. 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).

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Acute Myeloid/Lymphoblastic Leukemia Data Augmentation 2019-03-12

Acute Myeloid/Lymphoblastic Leukemia Data Augmentation

The AML/ALL Classifier Data Augmentation program applies 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 (AML/ALL) Leukemia AI Research Project.

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