Care of terminally ill / Palliative care

Digital Twin for a Multifunctional Technology of Flexible Assembly on a Mechatronics Line with Integrated Robotic Systems and Mobile Visual Sensor-Challenges towards Industry 5.0.

A digital twin for a multifunctional technology for flexible manufacturing on an assembly, disassemb...

Current Applications of Robot-Assisted Ultrasound Examination.

Despite advances in miniaturization and automation, the need for expert acquisition of a full echoca...

sEMG-Based Gain-Tuned Compliance Control for the Lower Limb Rehabilitation Robot during Passive Training.

The lower limb rehabilitation robot is a typical man-machine coupling system. Aiming at the problems...

A wearable soft robot that can alleviate the pain and fear of the wearer.

Social soft robotics may provide a new solution for alleviating human pain and fear. Here, we introd...

A Force-Feedback Methodology for Teleoperated Suturing Task in Robotic-Assisted Minimally Invasive Surgery.

With robotic-assisted minimally invasive surgery (RAMIS), patients and surgeons benefit from a reduc...

How feasible is end-to-end deep learning for clinical neuroimaging?

Using Artificial Neural Networks to Model Errors in Biochemical Manipulation of DNA Molecules.

In recent years, the non-biological applications of DNA molecules have made considerable progress; m...

A Robust End-to-End Deep Learning-Based Approach for Effective and Reliable BTD Using MR Images.

Detection of a brain tumor in the early stages is critical for clinical practice and survival rate. ...

Class-Imbalanced Deep Learning via a Class-Balanced Ensemble.

Class imbalance is a prevalent phenomenon in various real-world applications and it presents signifi...

GPCA: A Probabilistic Framework for Gaussian Process Embedded Channel Attention.

Channel attention mechanisms have been commonly applied in many visual tasks for effective performan...

De-identifying Australian hospital discharge summaries: An end-to-end framework using ensemble of deep learning models.

Electronic Medical Records (EMRs) contain clinical narrative text that is of great potential value t...

Sam's Net: A Self-Augmented Multistage Deep-Learning Network for End-to-End Reconstruction of Limited Angle CT.

Limited angle reconstruction is a typical ill-posed problem in computed tomography (CT). Given incom...

A Novel Velocity-Based Control in a Sensor Space for Parallel Manipulators.

It is a challenging task to track objects moving along an unknown trajectory. Conventional model-bas...

CollagenTransformer: End-to-End Transformer Model to Predict Thermal Stability of Collagen Triple Helices Using an NLP Approach.

Collagen is one of the most important structural proteins in biology, and its structural hierarchy p...

Self-supervised graph neural network with pre-training generative learning for recommendation systems.

The case assignment system is an essential system of case management and assignment within the procu...

Towards End-to-End Text Spotting in Natural Scenes.

Text spotting in natural scene images is of great importance for many image understanding tasks. It ...

Pancreatic Cancer Detection on CT Scans with Deep Learning: A Nationwide Population-based Study.

Background Approximately 40% of pancreatic tumors smaller than 2 cm are missed at abdominal CT. Purp...

MCluster-VAEs: An end-to-end variational deep learning-based clustering method for subtype discovery using multi-omics data.

The discovery of cancer subtypes based on unsupervised clustering helps in providing a precise diagn...

Optimization of Edge Resources for Deep Learning Application with Batch and Model Management.

As deep learning technology paves its way, real-world applications that make use of it become popula...

Frame-rate up-conversion detection based on convolutional neural network for learning spatiotemporal features.

With the advance in user-friendly and powerful video editing tools, anyone can easily manipulate vid...

Multi-Modal Graph Learning for Disease Prediction.

Benefiting from the powerful expressive capability of graphs, graph-based approaches have been popul...