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JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Feb 24, 2021
Recently, the coronavirus disease 2019 (COVID-19) has caused a pandemic disease in over 200 countries, influencing billions of humans. To control the infection, identifying and separating the infected people is the most crucial step. The main diagnos...
Neural state space alignment for magnitude generalization in humans and recurrent networks.
Neuron
Feb 23, 2021
A prerequisite for intelligent behavior is to understand how stimuli are related and to generalize this knowledge across contexts. Generalization can be challenging when relational patterns are shared across contexts but exist on different physical s...
Convolutional Neural Network-Based Deep Learning Model for Predicting Differential Suicidality in Depressive Patients Using Brain Generalized q-Sampling Imaging.
The Journal of clinical psychiatry
Feb 23, 2021
OBJECTIVE: Suicide is a priority health problem. Suicide assessment depends on imperfect clinician assessment with minimal ability to predict the risk of suicide. Machine learning/deep learning provides an opportunity to detect an individual at risk ...
Observational study on wearable biosensors and machine learning-based remote monitoring of COVID-19 patients.
Scientific reports
Feb 23, 2021
Patients infected with SARS-CoV-2 may deteriorate rapidly and therefore continuous monitoring is necessary. We conducted an observational study involving patients with mild COVID-19 to explore the potentials of wearable biosensors and machine learnin...
The DecNef collection, fMRI data from closed-loop decoded neurofeedback experiments.
Scientific data
Feb 23, 2021
Decoded neurofeedback (DecNef) is a form of closed-loop functional magnetic resonance imaging (fMRI) combined with machine learning approaches, which holds some promises for clinical applications. Yet, currently only a few research groups have had th...
Patients' Preferences for Artificial Intelligence Applications Versus Clinicians in Disease Diagnosis During the SARS-CoV-2 Pandemic in China: Discrete Choice Experiment.
Journal of medical Internet research
Feb 23, 2021
BACKGROUND: Misdiagnosis, arbitrary charges, annoying queues, and clinic waiting times among others are long-standing phenomena in the medical industry across the world. These factors can contribute to patient anxiety about misdiagnosis by clinicians...
Young Adult
Adult
Male
Middle Aged
China
Health Expenditures
Humans
Female
Diagnosis
Artificial Intelligence
SARS-CoV-2
Patient Preference
Pandemics
Choice Behavior
Aged
COVID-19
Diagnostic Techniques and Procedures
Aged, 80 and over
Physicians
Surveys and Questionnaires
Latent Class Analysis
Adolescent
Time Factors
Logistic Models
Deep-learning system for real-time differentiation between Crohn's disease, intestinal Behçet's disease, and intestinal tuberculosis.
Journal of gastroenterology and hepatology
Feb 20, 2021
BACKGROUND AND AIM: Pattern analysis of big data can provide a superior direction for the clinical differentiation of diseases with similar endoscopic findings. This study aimed to develop a deep-learning algorithm that performs differential diagnosi...
Facial geometric feature extraction based emotional expression classification using machine learning algorithms.
PloS one
Feb 18, 2021
Emotion plays a significant role in interpersonal communication and also improving social life. In recent years, facial emotion recognition is highly adopted in developing human-computer interfaces (HCI) and humanoid robots. In this work, a triangula...
Use and Control of Artificial Intelligence in Patients Across the Medical Workflow: Single-Center Questionnaire Study of Patient Perspectives.
Journal of medical Internet research
Feb 17, 2021
BACKGROUND: Artificial intelligence (AI) is gaining increasing importance in many medical specialties, yet data on patients' opinions on the use of AI in medicine are scarce.
Thyroid gland delineation in noncontrast-enhanced CTs using deep convolutional neural networks.
Physics in medicine and biology
Feb 16, 2021
The purpose of this study is to develop a deep learning method for thyroid delineation with high accuracy, efficiency, and robustness in noncontrast-enhanced head and neck CTs. The cross-sectional analysis consisted of six tests, including randomized...
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