Medical Ethics / Professional Responsibility

CAS: corpus of clinical cases in French.

BACKGROUND: Textual corpora are extremely important for various NLP applications as they provide information necessary for creating, setting and testing those applications and the corresponding tools. They are also crucial for designing reliable methods and reproducible results. Yet, in some areas, such as the medical area, due to confidentiality or to ethical reasons, it is complicated or even im...

Intermittent boundary stabilization of stochastic reaction-diffusion Cohen-Grossberg neural networks.

Cohen-Grossberg neural networks (CGNNs) play an important role in many applications and the stabilization of this system has been well studied. This study considers the exponential stabilization for stochastic reaction-diffusion Cohen-Grossberg neural networks (SRDCGNNs) by means of an aperiodically intermittent boundary control. Both SRDCGNNs without and with time-delays are discussed. By employi...

Attention-Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound Images.

Incorporating human domain knowledge for breast tumor diagnosis is challenging because shape, boundary, curvature, intensity or other common medical p...

Discretely-constrained deep network for weakly supervised segmentation.

An efficient strategy for weakly-supervised segmentation is to impose constraints or regularization priors on target regions. Recent efforts have focu...

Deep-learning-based accurate hepatic steatosis quantification for histological assessment of liver biopsies.

Hepatic steatosis droplet quantification with histology biopsies has high clinical significance for risk stratification and management of patients wit...

Evaluation of mental workload during automobile driving using one-class support vector machine with eye movement data.

The aim of this study is to investigate the usefulness of the anomaly detection method by one-class support vector machine (OCSVM) for the evaluation ...

Ultrasound Deep Learning for Wall Segmentation and Near-Wall Blood Flow Measurement.

Studies of medical flow imaging have technical limitations for accurate analysis of blood flow dynamics and vessel wall interaction at arteries. We pr...

On the boundary conditions of avoidance memory reconsolidation: An attractor network perspective.

The reconsolidation and extinction of aversive memories and their boundary conditions have been extensively studied. Knowing their network mechanisms ...

Applying Deep Neural Networks over Homomorphic Encrypted Medical Data.

In recent years, powered by state-of-the-art achievements in a broad range of areas, machine learning has received considerable attention from the hea...

CPFNet: Context Pyramid Fusion Network for Medical Image Segmentation.

Accurate and automatic segmentation of medical images is a crucial step for clinical diagnosis and analysis. The convolutional neural network (CNN) ap...

Optic Disc and Cup Image Segmentation Utilizing Contour-Based Transformation and Sequence Labeling Networks.

Optic disc (OD) and optic cup (OC) segmentation are important steps for automatic screening and diagnosing of optic nerve head abnormalities such as g...

Modeling the Research Landscapes of Artificial Intelligence Applications in Diabetes (GAP).

The rising prevalence and global burden of diabetes fortify the need for more comprehensive and effective management to prevent, monitor, and treat di...

Anonymization Through Data Synthesis Using Generative Adversarial Networks (ADS-GAN).

The medical and machine learning communities are relying on the promise of artificial intelligence (AI) to transform medicine through enabling more ac...

Imaging research in fibrotic lung disease; applying deep learning to unsolved problems.

Over the past decade, there has been a groundswell of research interest in computer-based methods for objectively quantifying fibrotic lung disease on...

Enabling a Single Deep Learning Model for Accurate Gland Instance Segmentation: A Shape-Aware Adversarial Learning Framework.

Segmenting gland instances in histology images is highly challenging as it requires not only detecting glands from a complex background but also separ...

Bimodal Automated Carotid Ultrasound Segmentation Using Geometrically Constrained Deep Neural Networks.

For asymptomatic patients suffering from carotid stenosis, the assessment of plaque morphology is an important clinical task which allows monitoring o...

A study of deep learning methods for de-identification of clinical notes in cross-institute settings.

BACKGROUND: De-identification is a critical technology to facilitate the use of unstructured clinical text while protecting patient privacy and confid...

3D nanostructural characterisation of grain boundaries in atom probe data utilising machine learning methods.

Boosting is a family of supervised learning algorithm that convert a set of weak learners into a single strong one. It is popular in the field of obje...

Real-Time Visual Tracking with Variational Structure Attention Network.

Online training framework based on discriminative correlation filters for visual tracking has recently shown significant improvement in both accuracy ...

Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks.

It remains challenging to automatically segment kidneys in clinical ultrasound (US) images due to the kidneys' varied shapes and image intensity distr...