Hematology

Lymphoma

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

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Showing 1849-1869 of 9,008 articles
Non-Invasive Modulation and Robotic Mapping of Motor Cortex in the Developing Brain.

Mapping the motor cortex with transcranial magnetic stimulation (TMS) has potential to interrogate m...

Machine learning-based radiomics strategy for prediction of cell proliferation in non-small cell lung cancer.

PURPOSE: To explore the feasibility and performance of machine learning-based radiomics classifier t...

A Machine Learning-Based Approach for the Prediction of Acute Coronary Syndrome Requiring Revascularization.

The aim of this study is to predict acute coronary syndrome (ACS) requiring revascularization in tho...

F-FDG-PET-based radiomics features to distinguish primary central nervous system lymphoma from glioblastoma.

The differential diagnosis of primary central nervous system lymphoma from glioblastoma multiforme (...

Application of artificial neural networks for Process Analytical Technology-based dissolution testing.

This work proposes the application of artificial neural networks (ANN) to non-destructively predict ...

Modelling disease risk for amyloid A (AA) amyloidosis in non-human primates using machine learning.

Amyloid A (AA) amyloidosis is found in humans and non-human primates, but quantifying disease risk ...

Detecting mitotic cells in HEp-2 images as anomalies via one class classifier.

We propose a novel framework for classification of mitotic v/s non-mitotic cells in a Computer Aided...

A Non-Invasive Continuous Blood Pressure Estimation Approach Based on Machine Learning.

Considering the existing issues of traditional blood pressure (BP) measurement methods and non-invas...

Non-steroid anti-inflammatory drugs reduce the efficacy of autologous blood pleurodesis.

BACKGROUND: This study aims to perform autologous blood pleurodesis in an animal model and investiga...

Non-Linear Dynamical Analysis of Resting Tremor for Demand-Driven Deep Brain Stimulation.

Parkinson's Disease (PD) is currently the second most common neurodegenerative disease. One of the m...

Assessing crop damage from dicamba on non-dicamba-tolerant soybean by hyperspectral imaging through machine learning.

BACKGROUND: Dicamba effectively controls several broadleaf weeds. The off-target drift of dicamba sp...

Non-Invasive Sensing of Nitrogen in Plant Using Digital Images and Machine Learning for ssp. L.

Monitoring plant nitrogen (N) in a timely way and accurately is critical for precision fertilization...

Rapid discrimination of multiple myeloma patients by artificial neural networks coupled with mass spectrometry of peripheral blood plasma.

Multiple myeloma (MM) is a highly heterogeneous disease of malignant plasma cells. Diagnosis and mon...

Multiclass Classifier for P-Glycoprotein Substrates, Inhibitors, and Non-Active Compounds.

P-glycoprotein (P-gp) is a transmembrane protein that actively transports a wide variety of chemical...

Prediction of pathologic stage in non-small cell lung cancer using machine learning algorithm based on CT image feature analysis.

PURPOSE: To explore imaging biomarkers that can be used for diagnosis and prediction of pathologic s...

Brain Tumor Segmentation Based on Improved Convolutional Neural Network in Combination with Non-quantifiable Local Texture Feature.

Accurate and reliable brain tumor segmentation is a critical component in cancer diagnosis. Accordin...

Machine Learning with Optical Phase Signatures for Phenotypic Profiling of Cell Lines.

Robust and reproducible profiling of cell lines is essential for phenotypic screening assays. The go...

Quantitative Prediction of the Landscape of T Cell Epitope Immunogenicity in Sequence Space.

Immunodominant T cell epitopes preferentially targeted in multiple individuals are the critical elem...

Plaque components segmentation in carotid artery on simultaneous non-contrast angiography and intraplaque hemorrhage imaging using machine learning.

PURPOSE: This study sought to determine the feasibility of using Simultaneous Non-contrast Angiograp...

Identification of Hürthle cell cancers: solving a clinical challenge with genomic sequencing and a trio of machine learning algorithms.

BACKGROUND: Identification of Hürthle cell cancers by non-operative fine-needle aspiration biopsy (F...

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