Care of terminally ill / Palliative care

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 portion of recent approaches offers individualized solutions based on specialized task-specific architectures or require refinement through non-end-to-end training. In this paper, we propose a new framework, named MedGAN, for medical image-to-image ...

A general end-to-end diagnosis framework for manufacturing systems.

The manufacturing sector is envisioned to be heavily influenced by artificial-intelligence-based technologies with the extraordinary increases in computational power and data volumes. A central challenge in the manufacturing sector lies in the requirement of a general framework to ensure satisfied diagnosis and monitoring performances in different manufacturing applications. Here, we propose a gen...

IoT-Enabled Dual-Arm Motion Capture and Mapping for Telerobotics in Home Care.

With the paradigm shift from hospital-centric healthcare to home-centric healthcare in Healthcare 4.0, healthcare robotics has become one of the faste...

Adaptive fuzzy flow rate control considering multifractal traffic modeling and 5G communications.

In this paper, we propose a predictive Generalized OBF (Orthonormal Basis Functions)-Fuzzy flow control scheme for the 5G downlink by deriving an expr...

Real-Time Extraction of Important Surgical Phases in Cataract Surgery Videos.

The present study aimed to conduct a real-time automatic analysis of two important surgical phases, which are continuous curvilinear capsulorrhexis (C...

Unsupervised 3D End-to-End Medical Image Registration With Volume Tweening Network.

3D medical image registration is of great clinical importance. However, supervised learning methods require a large amount of accurately annotated cor...

Identification and Quantification of Cardiovascular Structures From CCTA: An End-to-End, Rapid, Pixel-Wise, Deep-Learning Method.

OBJECTIVES: This study designed and evaluated an end-to-end deep learning solution for cardiac segmentation and quantification.

Fetal Congenital Heart Disease Echocardiogram Screening Based on DGACNN: Adversarial One-Class Classification Combined with Video Transfer Learning.

Fetal congenital heart disease (FHD) is a common and serious congenital malformation in children. In Asia, FHD birth defect rates have reached as high...

Spatio-Temporal Convolutional LSTMs for Tumor Growth Prediction by Learning 4D Longitudinal Patient Data.

Prognostic tumor growth modeling via volumetric medical imaging observations can potentially lead to better outcomes of tumor treatment management and...

Evaluating the performance of a predictive modeling approach to identifying members at high-risk of hospitalization.

To evaluate the risk-of-hospitalization (ROH) models developed at Blue Cross Blue Shield of Louisiana (BCBSLA) and compare this approach to the DxCG ...

EUSKOR: End-to-end coreference resolution system for Basque.

This paper describes the process of adapting the Stanford Coreference resolution module to the Basque language, taking into account the characteristic...

CIGuide: in situ augmented reality laser guidance.

PURPOSE : A robotic intraoperative laser guidance system with hybrid optic-magnetic tracking for skull base surgery is presented. It provides in situ ...

Deep Learning to Improve Breast Cancer Detection on Screening Mammography.

The rapid development of deep learning, a family of machine learning techniques, has spurred much interest in its application to medical imaging probl...

Indirect and direct training of spiking neural networks for end-to-end control of a lane-keeping vehicle.

Building spiking neural networks (SNNs) based on biological synaptic plasticities holds a promising potential for accomplishing fast and energy-effici...

Integrated Robotic Mini Bioreactor Platform for Automated, Parallel Microbial Cultivation With Online Data Handling and Process Control.

During process development, the experimental search space is defined by the number of experiments that can be performed in specific time frames but al...

Time series classification with Echo Memory Networks.

Echo state networks (ESNs) are randomly connected recurrent neural networks (RNNs) that can be used as a temporal kernel for modeling time series data...

FusionAtt: Deep Fusional Attention Networks for Multi-Channel Biomedical Signals.

Recently, pervasive sensing technologies have been widely applied to comprehensive patient monitoring in order to improve clinical treatment. Various ...

Recognition of 3'-end L1, Alu, processed pseudogenes, and mRNA stem-loops in the human genome using sequence-based and structure-based machine-learning models.

The role of 3'-end stem-loops in retrotransposition was experimentally demonstrated for transposons of various species, where LINE-SINE retrotransposo...

Perceptions of drones, digital adherence monitoring technologies and educational videos for tuberculosis control in remote Madagascar: a mixed-method study protocol.

INTRODUCTION: Poor road and communication infrastructure pose major challenges to tuberculosis (TB) control in many regions of the world. TB surveilla...

Sample Fusion Network: An End-to-End Data Augmentation Network for Skeleton-Based Human Action Recognition.

Data augmentation is a widely used technique for enhancing the generalization ability of deep neural networks for skeleton-based human action recognit...