Medical Ethics / Professional Responsibility

Ethical and Legal Challenges of Artificial Intelligence in Nuclear Medicine.

Artificial intelligence (AI) in nuclear medicine has gained significant traction and promises to be a disruptive, but innovative, technology. Recent developments in artificial neural networks, machine learning, and deep learning have ignited debate with respect to ethical and legal challenges associated with the use of AI in healthcare and medicine. While AI in nuclear medicine has the potential t...

Considerations for the Ethical Implementation of Psychological Assessment Through Social Media via Machine Learning.

The ubiquity of social media usage has led to exciting new technologies such as machine learning. Machine learning is poised to change many fields of health, including psychology. The wealth of information provided by each social media user in combination with machine learning technologies may pave the way for automated psychological assessment and diagnosis. Assessment of individuals' social medi...

Localization of Biobotic Insects Using Low-Cost Inertial Measurement Units.

Disaster robotics is a growing field that is concerned with the design and development of robots for disaster response and disaster recovery. These ro...

Pine Cone Detection Using Boundary Equilibrium Generative Adversarial Networks and Improved YOLOv3 Model.

The real-time detection of pine cones in Korean pine forests is not only the data basis for the mechanized picking of pine cones, but also one of the ...

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 testi...

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 s...

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...