Hematology

Lymphoma

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

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Radiomics features on non-contrast computed tomography predict early enlargement of spontaneous intracerebral hemorrhage.

OBJECTIVE: To explore the value of radiomics features on non-contrast computed tomography (NCCT) in ...

Non-fragile state estimation for fractional-order delayed memristive BAM neural networks.

This paper deals with the non-fragile state estimation problem for a class of fractional-order memri...

Non-Invasive Tools to Detect Smoke Contamination in Grapevine Canopies, Berries and Wine: A Remote Sensing and Machine Learning Modeling Approach.

Bushfires are becoming more frequent and intensive due to changing climate. Those that occur close t...

Mechanistic interpretation of non-coding variants for discovering transcriptional regulators of drug response.

BACKGROUND: Identification of functional non-coding variants and their mechanistic interpretation is...

scGen predicts single-cell perturbation responses.

Accurately modeling cellular response to perturbations is a central goal of computational biology. W...

Non-contact heart and respiratory rate monitoring of preterm infants based on a computer vision system: a method comparison study.

BACKGROUND: Non-contact heart rate (HR) and respiratory rate (RR) monitoring is necessary for preter...

Inferring the Disease-Associated miRNAs Based on Network Representation Learning and Convolutional Neural Networks.

Identification of disease-associated miRNAs (disease miRNAs) are critical for understanding etiology...

Data-driven self-calibration and reconstruction for non-cartesian wave-encoded single-shot fast spin echo using deep learning.

BACKGROUND: Current self-calibration and reconstruction methods for wave-encoded single-shot fast sp...

Identifying non-O157 Shiga toxin-producing Escherichia coli (STEC) using deep learning methods with hyperspectral microscope images.

Non-O157 Shiga toxin-producing Escherichia coli (STEC) serogroups such as O26, O45, O103, O111, O121...

Assessment of lipid peroxidation and artificial neural network models in early Alzheimer Disease diagnosis.

OBJECTIVE: Lipid peroxidation constitutes a molecular mechanism involved in early Alzheimer Disease ...

Using Deep Neural Networks to Reconstruct Non-uniformly Sampled NMR Spectra.

Non-uniform and sparse sampling of multi-dimensional NMR spectra has over the last decade become an ...

A radiomics approach based on support vector machine using MR images for preoperative lymph node status evaluation in intrahepatic cholangiocarcinoma.

: Accurate lymph node (LN) status evaluation for intrahepatic cholangiocarcinoma (ICC) patients is e...

Radiomics and machine learning of multisequence multiparametric prostate MRI: Towards improved non-invasive prostate cancer characterization.

PURPOSE: To develop and validate a classifier system for prediction of prostate cancer (PCa) Gleason...

Use of a Tracer-Specific Deep Artificial Neural Net to Denoise Dynamic PET Images.

Application of kinetic modeling (KM) on a voxel level in dynamic PET images frequently suffers from ...

Characterization of the non-stationary nature of steady-state visual evoked potentials using echo state networks.

State Visual Evoked Potentials (SSVEPs) arise from a resonance phenomenon in the visual cortex that ...

Efficiently searching through large tACS parameter spaces using closed-loop Bayesian optimization.

BACKGROUND: Selecting optimal stimulation parameters from numerous possibilities is a major obstacle...

Cancer classification and pathway discovery using non-negative matrix factorization.

OBJECTIVES: Extracting genetic information from a full range of sequencing data is important for und...

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