Lymphoma

Deep learning segmentation of orbital fat to calibrate conventional MRI for longitudinal studies.

In conventional non-quantitative magnetic resonance imaging, image contrast is consistent within ima...

Computational analysis of non-invasive deep brain stimulation based on interfering electric fields.

Neuromodulation modalities are used as effective treatments for some brain disorders. Non-invasive d...

Machine learning: assessing neurovascular signals in the prefrontal cortex with non-invasive bimodal electro-optical neuroimaging in opiate addiction.

Chronic and recurrent opiate use injuries brain tissue and cause serious pathophysiological changes ...

Machine Learning to Detect Alzheimer's Disease from Circulating Non-coding RNAs.

Blood-borne small non-coding (sncRNAs) are among the prominent candidates for blood-based diagnostic...

Cardio-respiratory signal extraction from video camera data for continuous non-contact vital sign monitoring using deep learning.

UNLABELLED: Non-contact vital sign monitoring enables the estimation of vital signs, such as heart r...

Dissection of gene expression datasets into clinically relevant interaction signatures via high-dimensional correlation maximization.

Gene expression is controlled by many simultaneous interactions, frequently measured collectively in...

Stable polyp-scene classification via subsampling and residual learning from an imbalanced large dataset.

This Letter presents a stable polyp-scene classification method with low false positive (FP) detecti...

Radiomics based on F-FDG PET/CT could differentiate breast carcinoma from breast lymphoma using machine-learning approach: A preliminary study.

PURPOSE: Our study assessed the ability F-fluorodeoxyglucose (FDG) positron emission tomography (PE...

Introducing of an integrated artificial neural network and Chou's pseudo amino acid composition approach for computational epitope-mapping of Crimean-Congo haemorrhagic fever virus antigens.

This study was aimed to introduce a novel algorithm for determining linear B- and T-cell epitopes fr...

MedGAN: Medical image translation using GANs.

Image-to-image translation is considered a new frontier in the field of medical image analysis, with...

Optimizing MRF-ASL scan design for precise quantification of brain hemodynamics using neural network regression.

PURPOSE: Arterial Spin Labeling (ASL) is a quantitative, non-invasive alternative for perfusion imag...

Convolutional Neural Networks for the Design and Analysis of Non-Fullerene Acceptors.

Convolutional neural network (CNN) is employed to construct generative and prediction models for the...

Non-Invasive Estimation of Hemoglobin Using a Multi-Model Stacking Regressor.

OBJECTIVE: We describe a novel machine-learning based method to estimate total Hemoglobin (Hb) using...

Ranking of non-coding pathogenic variants and putative essential regions of the human genome.

A gene is considered essential if loss of function results in loss of viability, fitness or in disea...

Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks.

Labeled medical imaging data is scarce and expensive to generate. To achieve generalizable deep lear...

Application of deep learning in aquatic bioassessment: Towards automated identification of non-biting midges.

Morphological species identification is often a difficult, expensive, and time-consuming process whi...

Algorithm-improved high-speed and non-invasive confocal Raman imaging of 2D materials.

Confocal Raman microscopy is important for characterizing 2D materials, but its low throughput signi...

The potential of machine learning to predict postoperative pancreatic fistula based on preoperative, non-contrast-enhanced CT: A proof-of-principle study.

BACKGROUND: Postoperative pancreatic fistula remains an unsolved challenge after pancreatoduodenecto...

Predictive models with the use of omics and supervised machine learning to diagnose non-alcoholic fatty liver disease: A "non-invasive alternative" to liver biopsy?

Non-invasive diagnosis of non-alcoholic steatohepatitis and fibrosis with the use of omics and supervised learning: A proof of concept study.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) affects 25-30% of the general population and i...

Clinical non-superiority of technology-assisted gait training with body weight support in patients with subacute stroke: A meta-analysis.

BACKGROUND: Technology-assisted gait training (TAGT) with body weight support (BWS) has been designe...