Lymphoma

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 (PET)/computed tomography (CT) radiomics to differentiate breast carcinoma from breast lymphoma using machine-learning approach.

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 from Crimean-Congo haemorrhagic fever virus (CCHFV) antigens. To this end, 387 approved B- and T-cell epitopes, as well as 331 non-epitope peptides from different serotypes of the virus were collected from IEDB database for generating of the train datasets. After that, the physicochemical properties o...

MedGAN: Medical image translation using GANs.

Image-to-image translation is considered a new frontier in the field of medical image analysis, with numerous potential applications. However, a large...

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 disease. This concept is well established for coding ge...

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 learning models large amounts of data are needed. Stan...

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 is characterized by the presence of non-alcoholic f...

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 constrained convex optimization, whose objective f...

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-ischaemic cardiomyopathy. We aim to examine the predicti...

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 applications. However, existing works primarily focused on ...

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 tumor target delineation and treatment delivery, thus ...

Effects of corn straw on dissipation of polycyclic aromatic hydrocarbons and potential application of backpropagation artificial neural network prediction model for PAHs bioremediation.

In order to provide a viable option for remediation of PAHs-contaminated soils, a greenhouse experiment was conducted to assess the effect of corn str...

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 when no transmission data or computed tomography (...

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 pore diameter (φ), pore density (ρ) and porosity (ν)...

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 white matter of the brain and in the spinal cord....

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 United States Preventative Services Task Forc...

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 increased image noise and low signal-to-noise ratio (SN...

Modelling and Optimizing Pyrene Removal from the Soil by Phytoremediation using Response Surface Methodology, Artificial Neural Networks, and Genetic Algorithm.

This study aimed to model and optimize pyrene removal from the soil contaminated by sorghum bicolor plant using Response Surface Methodology (RSM) and...

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 and pathogenesis. Most previous methods focus on ...

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 (AD) stages, and artificial neural network (ANN) a...

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 essential for surgical planning. This study aimed t...