Lymphoma

Real-Time Detection of Non-Stationary Objects Using Intensity Data in Automotive LiDAR SLAM.

This article aims at demonstrating the feasibility of modern deep learning techniques for the real-t...

Non-granular laterally spreading tumors: potential superficial cancers that artificial intelligence does not easily detect.

Validation of an artificial intelligence solution for acute triage and rule-out normal of non-contrast CT head scans.

PURPOSE: Non-contrast CT head scans provide rapid and accurate diagnosis of acute head injury; howev...

The Trials and Tribulations of Assembling Large Medical Imaging Datasets for Machine Learning Applications.

With vast interest in machine learning applications, more investigators are proposing to assemble la...

COVID-19 detection from lung CT-Scans using a fuzzy integral-based CNN ensemble.

The COVID-19 pandemic has collapsed the public healthcare systems, along with severely damaging the ...

Retrospective study of deep learning to reduce noise in non-contrast head CT images.

PURPOSE: Presented herein is a novel CT denoising method uses a skip residual encoder-decoder framew...

Outcome-based multiobjective optimization of lymphoma radiation therapy plans.

At its core, radiation therapy (RT) requires balancing therapeutic effects against risk of adverse e...

Effect of combining features generated through non-linear analysis and wavelet transform of EEG signals for the diagnosis of encephalopathy.

Electroencephalogram (EEG) signals portray hidden neuronal interactions in the brain and indicate br...

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data.

A variety of biological sequence classification tasks, such as species classification, gene function...

Predicting medication adherence using ensemble learning and deep learning models with large scale healthcare data.

Clinical studies from WHO have demonstrated that only 50-70% of patients adhere properly to prescrib...

Non-melanoma skin cancer diagnosis: a comparison between dermoscopic and smartphone images by unified visual and sonification deep learning algorithms.

PURPOSE: Non-melanoma skin cancer (NMSC) is the most frequent keratinocyte-origin skin tumor. It is ...

Non-invasive diagnostic tool for Parkinson's disease by sebum RNA profile with machine learning.

Parkinson's disease (PD) is a progressive neurodegenerative disease presenting with motor and non-mo...

functional cell phenotyping reveals microdomain networks in colorectal cancer recurrence.

Tumors are dynamic ecosystems comprising localized niches (microdomains), possessing distinct compos...

Machine learning risk prediction model for acute coronary syndrome and death from use of non-steroidal anti-inflammatory drugs in administrative data.

Our aim was to investigate the usefulness of machine learning approaches on linked administrative he...

Non-invasive health prediction from visually observable features.

The unprecedented development of Artificial Intelligence has revolutionised the healthcare industry...

Repurposing non-oncology small-molecule drugs to improve cancer therapy: Current situation and future directions.

Drug repurposing or repositioning has been well-known to refer to the therapeutic applications of a ...

Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning.

Exploiting photoplethysmography signals (PPG) for non-invasive blood pressure (BP) measurement is in...

A CNN-based method to reconstruct 3-D spine surfaces from US images in vivo.

Three-dimensional (3-D) reconstruction of the spine surface is of strong clinical relevance for the ...

Machine Learning-Based Radiomics Signatures for EGFR and KRAS Mutations Prediction in Non-Small-Cell Lung Cancer.

Early identification of epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncoge...

Two-phase non-invasive multi-disease detection via sublingual region.

Non-invasive multi-disease detection is an active technology that detects human diseases automatical...

A machine learning approach to predict extreme inactivity in COPD patients using non-activity-related clinical data.

Facilitating the identification of extreme inactivity (EI) has the potential to improve morbidity an...