Staffing & Scheduling

Technical note: Progressive deep learning: An accelerated training strategy for medical image segmentation.

BACKGROUND: Recent advancements in Deep Learning (DL) methodologies have led to state-of-the-art per...

A 3D Tumor-Mimicking In Vitro Drug Release Model of Locoregional Chemoembolization Using Deep Learning-Based Quantitative Analyses.

Primary liver cancer, with the predominant form as hepatocellular carcinoma (HCC), remains a worldwi...

Utilization of an attentive map to preserve anatomical features for training convolutional neural-network-based low-dose CT denoiser.

BACKGROUND: The purpose of a convolutional neural network (CNN)-based denoiser is to increase the di...

An Extended Spatial Transformer Convolutional Neural Network for Gesture Recognition and Self-Calibration Based on Sparse sEMG Electrodes.

sEMG-based gesture recognition is widely applied in human-machine interaction system by its unique a...

The Impact of Robotic-Assisted Total Knee Arthroplasty on Resident Training.

BACKGROUND: As robotic-assisted total knee replacement (rTKA) continues to gain popularity, the impa...

Leveraging Multiple Distinct EEG Training Sessions for Improvement of Spectral-Based Biometric Verification Results.

Most studies on EEG-based biometry recognition report results based on signal databases, with a limi...

Blinded Predictions and Post Hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models.

Accurate methods to predict solubility from molecular structure are highly sought after in the chemi...

Effect of hyper-parameters on the performance of ConvLSTM based deep neural network in crop classification.

Deep learning based data driven methods with multi-sensors spectro-temporal data are widely used for...

Effect of robot-assisted stair climbing training as part of a rehabilitation program to improve pulmonary function, gait performance, balance, and exercise capacity in a patient after severe coronavirus disease 2019: a case report.

BACKGROUND: Severe coronavirus disease 2019 (COVID-19) infection may decrease respiratory and physic...

Another Paradigm Shift? The Impact of Robotics in Stroke Intervention.

ARTIFICIAL INTELLIGENCE (AI) AS AN AID IN RESTORATIVE DENTISTRY IS PROMISING, BUT STILL A WORK IN PROGRESS.

ARTICLE TITLE AND BIBLIOGRAPHIC INFORMATION: Artificial intelligence applications in restorative den...

Systematic Evaluation of Local and Global Machine Learning Models for the Prediction of ADME Properties.

Machine learning (ML) has become an indispensable tool to predict absorption, distribution, metaboli...

Nurses' perception towards care robots and their work experience with socially assistive technology during COVID-19: A qualitative study.

This study aimed to explore nurses' perceptions towards care robots and their work experiences in ca...

Human Factor Engineering Research for Rehabilitation Robots: A Systematic Review.

The application of human factors engineering for rehabilitation robots is based on a "human-centered...

Retention time prediction for small samples based on integrating molecular representations and adaptive network.

Retention time (RT) can provide orthogonal information different from that of mass spectrometry and ...

Improving Anatomical Plausibility in Medical Image Segmentation via Hybrid Graph Neural Networks: Applications to Chest X-Ray Analysis.

Anatomical segmentation is a fundamental task in medical image computing, generally tackled with ful...

Algorithm Recommendation and Performance Prediction Using Meta-Learning.

In the last years, the number of machine learning algorithms and their parameters has increased sign...

SPIDE: A purely spike-based method for training feedback spiking neural networks.

Spiking neural networks (SNNs) with event-based computation are promising brain-inspired models for ...

Objective assessment of intraoperative skills for robot-assisted partial nephrectomy (RAPN).

RAPN training usually takes place in-vivo and methods vary across countries/institutions. No common ...

Deep-learning measurement of intracerebral haemorrhage with mixed precision training: a coarse-to-fine study.

AIM: To develop a unified deep-learning-based method for automated intracerebral haemorrhage (ICH) s...

Comparison of the Diagnostic Performance of Deep Learning Algorithms for Reducing the Time Required for COVID-19 RT-PCR Testing.

(1) Background: Rapid and accurate negative discrimination enables efficient management of scarce is...