Lymphoma

The Emerging Role of Long Non-Coding RNAs and MicroRNAs in Neurodegenerative Diseases: A Perspective of Machine Learning.

Neurodegenerative diseases (NDs) are characterized by progressive neuronal dysfunction and death of brain cells population. As the early manifestations of NDs are similar, their symptoms are difficult to distinguish, making the timely detection and discrimination of each neurodegenerative disorder a priority. Several investigations have revealed the importance of microRNAs and long non-coding RNAs...

FEA and Machine Learning Techniques for Hidden Structure Analysis.

This study focuses on investigating and predicting two hidden structures: plant root system architecture and non-visible bubbles in plexiglass. Current approaches are damaging, expensive, or time-consuming. Infrared imaging was used to study the root structure and depth of small plants and to detect the diameter and depth of bubbles in plexiglass. A finite element analysis (FEA) model was built to...

Detailed Assessment of Sleep Architecture With Deep Learning and Shorter Epoch-to-Epoch Duration Reveals Sleep Fragmentation of Patients With Obstructive Sleep Apnea.

Traditional sleep staging with non-overlapping 30-second epochs overlooks multiple sleep-wake transitions. We aimed to overcome this by analyzing the ...

Classification of glioblastoma versus primary central nervous system lymphoma using convolutional neural networks.

A subset of primary central nervous system lymphomas (PCNSL) are difficult to distinguish from glioblastoma multiforme (GBM) on magnetic resonance ima...

Intermediate-term survival of robot-assisted versus open radical cystectomy for muscle-invasive and high-risk non-muscle invasive bladder cancer in The Netherlands.

BACKGROUND: Radical cystectomy with pelvic lymph node dissection is the recommended treatment in non-metastatic muscle-invasive bladder cancer (MIBC)....

Deep learning approach to predict sentinel lymph node status directly from routine histology of primary melanoma tumours.

AIM: Sentinel lymph node status is a central prognostic factor for melanomas. However, the surgical excision involves some risks for affected patients...

Energy optimization from a binary mixture of non-edible oilseeds pyrolysis: Kinetic triplets analysis using Thermogravimetric Analyser and prediction modeling by Artificial Neural Network.

Pyrolysis kinetics and thermodynamic parameters of two non-edible seeds, Pongamia pinnata (PP) and Sapindus emarginatus (SE), and their blend in the r...

Predicting pathogenic non-coding SVs disrupting the 3D genome in 1646 whole cancer genomes using multiple instance learning.

Over the past years, large consortia have been established to fuel the sequencing of whole genomes of many cancer patients. Despite the increased abun...

Rapid high-quality PET Patlak parametric image generation based on direct reconstruction and temporal nonlocal neural network.

Parametric imaging based on dynamic positron emission tomography (PET) has wide applications in neurology. Compared to indirect methods, direct recons...

Fuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans.

COVID-19 has crippled the world's healthcare systems, setting back the economy and taking the lives of several people. Although potential vaccines are...

Assessment of deep learning-based PET attenuation correction frameworks in the sinogram domain.

This study set out to investigate various deep learning frameworks for PET attenuation correction in the sinogram domain. Different models for both ti...

Valorization of groundnut shell via pyrolysis: Product distribution, thermodynamic analysis, kinetic estimation, and artificial neural network modeling.

Pyrolysis of agricultural biomass is a promising technique for producing renewable energy and effectively managing solid waste. In this study, groundn...

Deep learning-based identification of acute ischemic core and deficit from non-contrast CT and CTA.

The accurate identification of irreversible infarction and salvageable tissue is important in planning the treatments for acute ischemic stroke (AIS) ...

A computational pipeline for data augmentation towards the improvement of disease classification and risk stratification models: A case study in two clinical domains.

Virtual population generation is an emerging field in data science with numerous applications in healthcare towards the augmentation of clinical resea...

Spectral embedding network for attributed graph clustering.

Attributed graph clustering aims to discover node groups by utilizing both graph structure and node features. Recent studies mostly adopt graph neural...

Clustering-Based Dual Deep Learning Architecture for Detecting Red Blood Cells in Malaria Diagnostic Smears.

Computer-assisted algorithms have become a mainstay of biomedical applications to improve accuracy and reproducibility of repetitive tasks like manual...

A machine learning approach to screen for preclinical Alzheimer's disease.

Combining multimodal biomarkers could help in the early diagnosis of Alzheimer's disease (AD). We included 304 cognitively normal individuals from the...

Urine leak after robotic radical prostatectomy: not all urine leaks come from the anastomosis.

Radical prostatectomy is the gold standard in patients that are surgical candidates with localized prostate cancer. While most postoperative urine lea...

Brain graph super-resolution using adversarial graph neural network with application to functional brain connectivity.

Brain image analysis has advanced substantially in recent years with the proliferation of neuroimaging datasets acquired at different resolutions. Whi...

Non-Destructive Detection Pilot Study of Vegetable Organic Residues Using VNIR Hyperspectral Imaging and Deep Learning Techniques.

Contamination is a critical issue that affects food consumption adversely. Therefore, efficient detection and classification of food contaminants are ...