Lymphoma

New technologies in breast cancer sentinel lymph node biopsy; from the current gold standard to artificial intelligence.

Sentinel lymph node biopsy is an important diagnostic procedure performed in early breast cancer pat...

Improving disaggregation models of malaria incidence by ensembling non-linear models of prevalence.

Maps of disease burden are a core tool needed for the control and elimination of malaria. Reliable r...

Comparison study of classification methods of intramuscular electromyography data for non-human primate model of traumatic spinal cord injury.

Traumatic spinal cord injury is a serious neurological disorder. Patients experience a plethora of s...

What is the optimal surveillance strategy for non-dysplastic Barrett's esophagus?

PURPOSE OF REVIEW: There is conflicting data on the effectiveness of the currently recommended endos...

F-FDG PET-guided diffusion tractography reveals white matter abnormalities around the epileptic focus in medically refractory epilepsy: implications for epilepsy surgical evaluation.

BACKGROUND: Hybrid PET/MRI can non-invasively improve localization and delineation of the epileptic ...

Pinning bipartite synchronization for inertial coupled delayed neural networks with signed digraph via non-reduced order method.

The study investigates bipartite synchronization of inertial coupled delayed neural networks (ICDNNs...

Effects of non-facilitated meaningful activities for people with dementia in long-term care facilities: A systematic review.

This systematic review sought to evaluate the effectiveness of non-facilitated meaningful activities...

Ab initio GO-based mining for non-tandem-duplicated functional clusters in three model plant diploid genomes.

A functional Non-Tandem Duplicated Cluster (FNTDC) is a group of non-tandem-duplicated genes that ar...

Deep-Learning F-FDG Uptake Classification Enables Total Metabolic Tumor Volume Estimation in Diffuse Large B-Cell Lymphoma.

Total metabolic tumor volume (TMTV), calculated from F-FDG PET/CT baseline studies, is a prognostic ...

DeepDTI: High-fidelity six-direction diffusion tensor imaging using deep learning.

Diffusion tensor magnetic resonance imaging (DTI) is unsurpassed in its ability to map tissue micros...

Development and Validation of a Deep Learning Model for Non-Small Cell Lung Cancer Survival.

IMPORTANCE: There is a lack of studies exploring the performance of a deep learning survival neural ...

Prognosis and Treatment of Non-Small Cell Lung Cancer in the Age of Deep Learning.

Using Artificial Intelligence for Pattern Recognition in a Sports Context.

Optimizing athlete's performance is one of the most important and challenging aspects of coaching. P...

Non-invasive identification of swallows via deep learning in high resolution cervical auscultation recordings.

High resolution cervical auscultation is a very promising noninvasive method for dysphagia screening...

Experimental Data Based Machine Learning Classification Models with Predictive Ability to Select in Vitro Active Antiviral and Non-Toxic Essential Oils.

In the last decade essential oils have attracted scientists with a constant increase rate of more th...

Performance improvement of mediastinal lymph node severity detection using GAN and Inception network.

BACKGROUND AND OBJECTIVE: In lung cancer, the determination of mediastinal lymph node (MLN) status a...

Integrating 3D Model Representation for an Accurate Non-Invasive Assessment of Pressure Injuries with Deep Learning.

Pressure injuries represent a major concern in many nations. These wounds result from prolonged pres...

Machine learning provides evidence that stroke risk is not linear: The non-linear Framingham stroke risk score.

Current stroke risk assessment tools presume the impact of risk factors is linear and cumulative. Ho...

Non - invasive modelling methodology for the diagnosis of coronary artery disease using fuzzy cognitive maps.

Cardiovascular diseases (CVD) and strokes produce immense health and economic burdens globally. Coro...

Applications of Machine Learning to In Silico Quantification of Chemicals without Analytical Standards.

Non-targeted analysis provides a comprehensive approach to analyze environmental and biological samp...

Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms.

Deep neural networks (DNN) are state-of-the-art machine learning algorithms that can be learned to s...