This blog category focuses on the research were are doing related to detection and early detection of leukemia.

So far our research in this area covers using Convolutional Neural Networks (CNNs) & Generative Adversarial Networks (GANs) for detecting Acute Lymphoblastic Leukemia.

A complete guide to linear regression using gene expression data for Acute Myeloid Leukemia: Part 2 — Fit and algorithm evaluation 2021-04-23

A complete guide to linear regression using gene expression data for Acute Myeloid Leukemia: Part 2 — Fit and algorithm evaluation

How to fit a linear regression model to predict Acute Myeloid Leukemia, how to evaluate the results. Moreover, I will discuss the plots to investigate the algorithm results and solutions to the most common problems.

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A complete guide to linear regression using gene expression data for Acute Myeloid Leukemia: Part 1 - An introduction 2021-04-03

A complete guide to linear regression using gene expression data for Acute Myeloid Leukemia: Part 1 - An introduction

Discusses how to use linear regression with transcriptomic data for Acute Myeloid Leukemia, introducing linear regression and the math behind it.

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DBSCAN and Gaussian Mixtures with gene expression data for Acute Myeloid Leukemia 2021-03-19

DBSCAN and Gaussian Mixtures with gene expression data for Acute Myeloid Leukemia

Discusses DBSCAN and Gaussian Mixtures with gene expression data for Acute Myeloid Leukemia.

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xDNN for SARS-CoV-2 identification in patient CT scans 2021-02-22

xDNN for SARS-CoV-2 identification in patient CT scans

This article is based on the work of Nitin Mane and his GitHub release: HIAS COVID-19 xDNN Classifier.

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Complexity reduction techniques with gene expression data 2021-02-08

Complexity reduction techniques with gene expression data

This tutorial will focus on different reduction complex techniques using gene expression data. In this tutorial and in the following I will use data from acute myeloid leukemia (AML), which is one of the most fatal malignancies.

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Artificial intelligence in leukemia 2020-10-30

Artificial intelligence in leukemia

In this article our immunology and bioinformatics expert Salvatore Raieli, focuses on other tasks and other type of data exploited in deep learning (DL) in hematology. As aforementioned, deep learning can be useful in many tasks related to leukemia. In the precedent review we focused on image data analysis. Here we will consider different other data sources and application such as therapy selection, differential diagnosis, risk predictions.

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Medical image diagnosis in leukemia 2020-10-20

Medical image diagnosis in leukemia

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.

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Acute Myeloid Leukemia: A general introduction 2020-10-09

Acute Myeloid Leukemia: A general introduction

In this article our immunology and bioinformatics expert Salvatore Raieli describes briefly what AML is, the current classification, current therapies available, and open questions.

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Acute Lymphoblastic Leukemia detection with Tensorflow 2020-03-06

Acute Lymphoblastic Leukemia detection with Tensorflow

The final classifier achieves 98 (97.979)% using Tensorflow 2 & Ubuntu/GTX 1050 ti . You can run the classifier independently and classify local images, serve an API endpoint for HTTP requests, or you can use it as part of the VR experience which will be uploaded soon.

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Acute Lymphoblastic Leukemia Papers Evaluation Part 2 Tensorflow 2.0 2020-01-19

Acute Lymphoblastic Leukemia Papers Evaluation Part 2 Tensorflow 2.0

Here we will train the network we created in part 1, using the augmented dataset proposed in the Leukemia Blood Cell Image Classification Using Convolutional Neural Network paper by T. T. P. Thanh, Caleb Vununu, Sukhrob Atoev, Suk-Hwan Lee, and Ki-Ryong Kwon.

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Acute Lymphoblastic Leukemia Papers Evaluation Part 1 2020-01-19

Acute Lymphoblastic Leukemia Papers Evaluation Part 1

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.

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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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