Care of terminally ill / Palliative care

Multi-Scale Context-Guided Deep Network for Automated Lesion Segmentation With Endoscopy Images of Gastrointestinal Tract.

Accurate lesion segmentation based on endoscopy images is a fundamental task for the automated diagnosis of gastrointestinal tract (GI Tract) diseases. Previous studies usually use hand-crafted features for representing endoscopy images, while feature definition and lesion segmentation are treated as two standalone tasks. Due to the possible heterogeneity between features and segmentation models, ...

Large-Scale Modeling of Multispecies Acute Toxicity End Points Using Consensus of Multitask Deep Learning Methods.

Computational methods to predict molecular properties regarding safety and toxicology represent alternative approaches to expedite drug development, screen environmental chemicals, and thus significantly reduce associated time and costs. There is a strong need and interest in the development of computational methods that yield reliable predictions of toxicity, and many approaches, including the re...

The Design and Development of a Personalized Leisure Time Physical Activity Application Based on Behavior Change Theories, End-User Perceptions, and Principles From Empirical Data Mining.

Many adults do not reach the recommended physical activity (PA) guidelines, which can lead to serious health problems. A promising method to increase...

An End-to-End Foreground-Aware Network for Person Re-Identification.

Person re-identification is a crucial task of identifying pedestrians of interest across multiple surveillance camera views. For person re-identificat...

4D deep image prior: dynamic PET image denoising using an unsupervised four-dimensional branch convolutional neural network.

Although convolutional neural networks (CNNs) demonstrate the superior performance in denoising positron emission tomography (PET) images, a supervise...

DaNet: dose-aware network embedded with dose-level estimation for low-dose CT imaging.

Many deep learning (DL)-based image restoration methods for low-dose CT (LDCT) problems directly employ the end-to-end networks on low-dose training d...

Fully end-to-end deep-learning-based diagnosis of pancreatic tumors.

Artificial intelligence can facilitate clinical decision making by considering massive amounts of medical imaging data. Various algorithms have been i...

Developing a Qualification and Verification Strategy for Digital Tissue Image Analysis in Toxicological Pathology.

Digital tissue image analysis is a computational method for analyzing whole-slide images and extracting large, complex, and quantitative data sets. Ho...

Application of machine learning to improve dairy farm management: A systematic literature review.

In recent years, several researchers and practitioners applied machine learning algorithms in the dairy farm context and discussed several solutions t...

Deep multi-kernel auto-encoder network for clustering brain functional connectivity data.

In this study, we propose a deep-learning network model called the deep multi-kernel auto-encoder clustering network (DMACN) for clustering functional...

A dual-domain deep learning-based reconstruction method for fully 3D sparse data helical CT.

Helical CT has been widely used in clinical diagnosis. In this work, we focus on a new prototype of helical CT, equipped with sparsely spaced multidet...

LSTM-Based End-to-End Framework for Biomedical Event Extraction.

Biomedical event extraction plays an important role in the extraction of biological information from large-scale scientific publications. However, mos...

An end-to-end breast tumour classification model using context-based patch modelling - A BiLSTM approach for image classification.

Researchers working on computational analysis of Whole Slide Images (WSIs) in histopathology have primarily resorted to patch-based modelling due to l...

End-to-End Deep Learning Model for Predicting Treatment Requirements in Neovascular AMD From Longitudinal Retinal OCT Imaging.

Neovascular age-related macular degeneration (nAMD) is nowadays successfully treated with anti-VEGF substances, but inter-individual treatment require...

Task-Driven Learned Hyperspectral Data Reduction Using End-to-End Supervised Deep Learning.

An important challenge in hyperspectral imaging tasks is to cope with the large number of spectral bins. Common spectral data reduction methods do not...

Systematic Identification of Molecular Targets and Pathways Related to Human Organ Level Toxicity.

The mechanisms leading to organ level toxicities are poorly understood. In this study, we applied an integrated approach to deduce the molecular targe...

"Fast deep learning computer-aided diagnosis of COVID-19 based on digital chest x-ray images".

Coronavirus disease 2019 (COVID-19) is a novel harmful respiratory disease that has rapidly spread worldwide. At the end of 2019, COVID-19 emerged as ...

Training confounder-free deep learning models for medical applications.

The presence of confounding effects (or biases) is one of the most critical challenges in using deep learning to advance discovery in medical imaging ...

Estimation of End-Diastole in Cardiac Spectral Doppler Using Deep Learning.

Electrocardiogram (ECG) is often used together with a spectral Doppler ultrasound to separate heart cycles by determining the end-diastole locations. ...

LU-Net: A Multistage Attention Network to Improve the Robustness of Segmentation of Left Ventricular Structures in 2-D Echocardiography.

Segmentation of cardiac structures is one of the fundamental steps to estimate volumetric indices of the heart. This step is still performed semiautom...