Care of terminally ill / Palliative care

Sodium hydroxide-induced esophageal stricture via an endoscopic injection needle: a novel rabbit model of corrosive injury.

Benign strictures of the esophagus are commonly encountered in clinical practice and are difficult to manage conservatively. This study aimed to establish a novel animal model of benign esophageal stricture by using corrosive-induced injury in rabbits with an injection of sodium hydroxide (NaOH) via a self-made endoscopic injection needle. Corrosive injury of the esophagus was induced in 10 rabb...

Exploiting machine learning for end-to-end drug discovery and development.

A variety of machine learning methods such as naive Bayesian, support vector machines and more recently deep neural networks are demonstrating their utility for drug discovery and development. These leverage the generally bigger datasets created from high-throughput screening data and allow prediction of bioactivities for targets and molecular properties with increased levels of accuracy. We have ...

End-to-End Differentiable Learning of Protein Structure.

Predicting protein structure from sequence is a central challenge of biochemistry. Co-evolution methods show promise, but an explicit sequence-to-stru...

TSE-CNN: A Two-Stage End-to-End CNN for Human Activity Recognition.

Human activity recognition has been widely used in healthcare applications such as elderly monitoring, exercise supervision, and rehabilitation monito...

Applications of an interaction, process, integration and intelligence (IPII) design approach for ergonomics solutions.

This paper first reviews current ergonomics design approaches in delivering digital solutions to achieve a unified experience from interaction and bus...

Upper limb robot-assisted rehabilitation versus physical therapy on subacute stroke patients: A follow-up study.

This study aims to analyse the long-term effects (6 months follow-up) of upper limb Robot-assisted Therapy (RT) compared to Traditional physical Thera...

DeepPET: A deep encoder-decoder network for directly solving the PET image reconstruction inverse problem.

The purpose of this research was to implement a deep learning network to overcome two of the major bottlenecks in improved image reconstruction for cl...

Training Convolutional Neural Networks and Compressed Sensing End-to-End for Microscopy Cell Detection.

Automated cell detection and localization from microscopy images are significant tasks in biomedical research and clinical practice. In this paper, we...

A novel end-to-end brain tumor segmentation method using improved fully convolutional networks.

Accurate brain magnetic resonance imaging (MRI) tumor segmentation continues to be an active research topic in medical image analysis since it provide...

Learning joint space-time-frequency features for EEG decoding on small labeled data.

Brain-computer interfaces (BCIs), which control external equipment using cerebral activity, have received considerable attention recently. Translating...

Towards end-to-end likelihood-free inference with convolutional neural networks.

Complex simulator-based models with non-standard sampling distributions require sophisticated design choices for reliable approximate parameter infere...

End-to-End Active Object Tracking and Its Real-World Deployment via Reinforcement Learning.

We study active object tracking, where a tracker takes visual observations (i.e., frame sequences) as input and produces the corresponding camera cont...

Acceleration of spleen segmentation with end-to-end deep learning method and automated pipeline.

Delineation of Computed Tomography (CT) abdominal anatomical structure, specifically spleen segmentation, is useful for not only measuring tissue volu...

SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging.

Automatic sleep staging has been often treated as a simple classification problem that aims at determining the label of individual target polysomnogra...

Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals.

Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject's motor intention i...

Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network.

Computerized electrocardiogram (ECG) interpretation plays a critical role in the clinical ECG workflow. Widely available digital ECG data and the algo...

Towards cross-modal organ translation and segmentation: A cycle- and shape-consistent generative adversarial network.

Synthesized medical images have several important applications. For instance, they can be used as an intermedium in cross-modality image registration ...

Comparison of Muscular Activity and Movement Performance in Robot-Assisted and Freely Performed Exercises.

End-effector-based robotic systems are, in particular, suitable for extending physical therapy in stroke rehabilitation. An adequate therapy and thus ...

Natural language generation for electronic health records.

One broad goal of biomedical informatics is to generate fully-synthetic, faithfully representative electronic health records (EHRs) to facilitate data...

Multi-level features combined end-to-end learning for automated pathological grading of breast cancer on digital mammograms.

We propose to discriminate the pathological grades directly on digital mammograms instead of pathological images. An end-to-end learning algorithm bas...