Hematology

Lymphoma

Latest AI and machine learning research in lymphoma for healthcare professionals.

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Artificial Intelligence based Models for Screening of Hematologic Malignancies using Cell Population Data.

Cell Population Data (CPD) provides various blood cell parameters that can be used for differential ...

Variational approximation error in non-negative matrix factorization.

Non-negative matrix factorization (NMF) is a knowledge discovery method that is used in many fields....

Non-Standardized Patch-Based ECG Lead Together With Deep Learning Based Algorithm for Automatic Screening of Atrial Fibrillation.

This study was to assess the feasibility of using non-standardized single-lead electrocardiogram (EC...

Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning.

Non-small cell lung cancer (NSCLC) is one of the most common lung cancers worldwide. Accurate progno...

Statistical Modeling of Longitudinal Data with Non-ignorable Non-monotone Missingness with Semiparametric Bayesian and Machine Learning Components.

In longitudinal studies, outcomes are measured repeatedly over time and it is common that not all th...

Recognition of Common Non-Normal Walking Actions Based on Relief-F Feature Selection and Relief-Bagging-SVM.

Action recognition algorithms are widely used in the fields of medical health and pedestrian dead re...

Deep learning-based attenuation correction in the absence of structural information for whole-body positron emission tomography imaging.

Deriving accurate structural maps for attenuation correction (AC) of whole-body positron emission to...

Machine learning methods for microbiome studies.

Researches on the microbiome have been actively conducted worldwide and the results have shown human...

End-to-end semantic segmentation of personalized deep brain structures for non-invasive brain stimulation.

Electro-stimulation or modulation of deep brain regions is commonly used in clinical procedures for ...

Effects of Deep Learning Reconstruction Technique in High-Resolution Non-contrast Magnetic Resonance Coronary Angiography at a 3-Tesla Machine.

PURPOSE: To evaluate the effects of deep learning reconstruction (DLR) in qualitative and quantitati...

Radiogenomic Models Using Machine Learning Techniques to Predict EGFR Mutations in Non-Small Cell Lung Cancer.

BACKGROUND: The purpose of this study was to build radiogenomics models from texture signatures deri...

Prognostic value of anthropometric measures extracted from whole-body CT using deep learning in patients with non-small-cell lung cancer.

INTRODUCTION: The aim of the study was to extract anthropometric measures from CT by deep learning a...

Control of hyperparathyroidism with the intravenous calcimimetic etelcalcetide in dialysis patients adherent and non-adherent to oral calcimimetics.

BACKGROUND: In dialysis patients, non-adherence to oral cinacalcet adds complexity to the control of...

Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks.

The goal of this study was to compare the predictive performance of artificial neural networks (ANNs...

Reconstruction of undersampled 3D non-Cartesian image-based navigators for coronary MRA using an unrolled deep learning model.

PURPOSE: To rapidly reconstruct undersampled 3D non-Cartesian image-based navigators (iNAVs) using a...

Machine Learning for Detecting Early Infarction in Acute Stroke with Non-Contrast-enhanced CT.

Background Identifying the presence and extent of infarcted brain tissue at baseline plays a crucial...

Deep Learning-Based Development of Personalized Human Head Model With Non-Uniform Conductivity for Brain Stimulation.

Electromagnetic stimulation of the human brain is a key tool for neurophysiological characterization...

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