Hematology

Lymphoma

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

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A consensus algorithm based on collective neurodynamic system for distributed optimization with linear and bound constraints.

In this paper, an algorithm based on collective neurodynamic system is investigated for distributed ...

A speckle-tracking strain-based artificial neural network model to differentiate cardiomyopathy type.

In heart failure, invasive angiography is often employed to differentiate ischaemic from non-ischae...

Realistic spiking neural network: Non-synaptic mechanisms improve convergence in cell assembly.

Learning in neural networks inspired by brain tissue has been studied for machine learning applicati...

A Super-Learner Model for Tumor Motion Prediction and Management in Radiation Therapy: Development and Feasibility Evaluation.

In cancer radiation therapy, large tumor motion due to respiration can lead to uncertainties in tumo...

Physiological responses and perceived exertion during robot-assisted treadmill walking in non-ambulatory stroke survivors.

PURPOSE: To examine physiological responses and perceived exertion during robot-assisted treadmill w...

Independent brain F-FDG PET attenuation correction using a deep learning approach with Generative Adversarial Networks.

OBJECTIVE: Attenuation correction (AC) of positron emission tomography (PET) data poses a challenge ...

Non-faradaic electrochemical impedimetric profiling of procalcitonin and C-reactive protein as a dual marker biosensor for early sepsis detection.

In this work, we demonstrate a robust, dual marker, biosensing strategy for specific and sensitive e...

Leveraging implicit expert knowledge for non-circular machine learning in sepsis prediction.

Sepsis is the leading cause of death in non-coronary intensive care units. Moreover, a delay of anti...

Artificial neural network to estimate micro-architectural properties of cortical bone using ultrasonic attenuation: A 2-D numerical study.

The goal of this study is to estimate micro-architectural parameters of cortical porosity such as po...

Event-triggered passivity and synchronization of delayed multiple-weighted coupled reaction-diffusion neural networks with non-identical nodes.

This paper solves the event-triggered passivity and synchronization problems for delayed multiple-we...

Electron density learning of non-covalent systems.

Chemists continuously harvest the power of non-covalent interactions to control phenomena in both th...

Stacked Bidirectional Convolutional LSTMs for Deriving 3D Non-Contrast CT From Spatiotemporal 4D CT.

The imaging workup in acute stroke can be simplified by deriving non-contrast CT (NCCT) from CT perf...

Removing segmentation inconsistencies with semi-supervised non-adjacency constraint.

The advent of deep learning has pushed medical image analysis to new levels, rapidly replacing more ...

Estimating daily PM concentrations in New York City at the neighborhood-scale: Implications for integrating non-regulatory measurements.

Previous PM related epidemiological studies mainly relied on data from sparse regulatory monitors to...

Predicting PET-derived demyelination from multimodal MRI using sketcher-refiner adversarial training for multiple sclerosis.

Multiple sclerosis (MS) is the most common demyelinating disease. In MS, demyelination occurs in the...

Scoring colorectal cancer risk with an artificial neural network based on self-reportable personal health data.

Colorectal cancer (CRC) is third in prevalence and mortality among all cancers in the US. Currently,...

The virtual doctor: An interactive clinical-decision-support system based on deep learning for non-invasive prediction of diabetes.

Artificial intelligence (AI) will pave the way to a new era in medicine. However, currently availabl...

A non-linear mathematical model using optical sensor to predict heart decellularization efficacy.

One of the main problems of the decellularization technique is the subjectivity of the final evaluat...

An investigation of quantitative accuracy for deep learning based denoising in oncological PET.

Reducing radiation dose is important for PET imaging. However, reducing injection doses causes incre...

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