Care of terminally ill / Palliative care

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 pr...

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 ...

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 b...

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...

"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 w...

Machine learning algorithm for early detection of end-stage renal disease.

BACKGROUND: End stage renal disease (ESRD) describes the most severe stage of chronic kidney disease...

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...

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...

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 ...

Can We Ditch Feature Engineering? End-to-End Deep Learning for Affect Recognition from Physiological Sensor Data.

To further extend the applicability of wearable sensors in various domains such as mobile health sys...

Dynamic MRI reconstruction with end-to-end motion-guided network.

Temporal correlation in dynamic magnetic resonance imaging (MRI), such as cardiac MRI, is informativ...

Cancer classification based on chromatin accessibility profiles with deep adversarial learning model.

Given the complexity and diversity of the cancer genomics profiles, it is challenging to identify di...

Role of Intravenous Ascorbic Acid in the Management of Anemia in Hemodialysis Patients.

INTRODUCTION: Patients with end-stage kidney disease (ESKD) suffer from functional iron deficiency w...

Automated Aneurysm Detection: Emerging from the Shallow End of the Deep Learning Pool.

Measuring and Preventing COVID-19 Using the SIR Model and Machine Learning in Smart Health Care.

COVID-19 presents an urgent global challenge because of its contagious nature, frequently changing c...

Deep learning for digitizing highly noisy paper-based ECG records.

Electrocardiography (ECG) is essential in many heart diseases. However, some ECGs are recorded by pa...

Mapping and Discriminating Rural Settlements Using Gaofen-2 Images and a Fully Convolutional Network.

New ongoing rural construction has resulted in an extensive mixture of new settlements with old ones...

Deep convolutional neural network based on adaptive gradient optimizer for fault detection in SCIM.

Early fault detection in squirrel cage induction motor (SCIM) can minimize the downtime and maximize...

The prognostic role of end-of-treatment FDG-PET/CT in diffuse large B cell lymphoma: a pilot study application of neural networks to predict time-to-event.

PURPOSE: To evaluate the prognostic role of end-of-treatment (EoT) FDG-PET/CT parameters in diffuse ...

Associated Learning: Decomposing End-to-End Backpropagation Based on Autoencoders and Target Propagation.

Backpropagation (BP) is the cornerstone of today's deep learning algorithms, but it is inefficient p...

On the effects of memory and topology on the controllability of complex dynamical networks.

Recent advances in network science, control theory, and fractional calculus provide us with mathemat...