AIMC Topic: Deep Learning

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Generation of synthetic PET images of synaptic density and amyloid from F-FDG images using deep learning.

Medical physics
PURPOSE: Positron emission tomography (PET) imaging with various tracers is increasingly used in Alzheimer's disease (AD) studies. However, access to PET scans using new or less-available tracers with sophisticated synthesis and short half-life isoto...

A Study of Deep Learning-Based Face Recognition Models for Sibling Identification.

Sensors (Basel, Switzerland)
Accurate identification of siblings through face recognition is a challenging task. This is predominantly because of the high degree of similarities among the faces of siblings. In this study, we investigate the use of state-of-the-art deep learning ...

Attention-RefNet: Interactive Attention Refinement Network for Infected Area Segmentation of COVID-19.

IEEE journal of biomedical and health informatics
COVID-19 pneumonia is a disease that causes an existential health crisis in many people by directly affecting and damaging lung cells. The segmentation of infected areas from computed tomography (CT) images can be used to assist and provide useful in...

Interpretable and Lightweight 3-D Deep Learning Model for Automated ACL Diagnosis.

IEEE journal of biomedical and health informatics
We propose an interpretable and lightweight 3D deep neural network model that diagnoses anterior cruciate ligament (ACL) tears from a knee MRI exam. Previous works focused primarily on achieving better diagnostic accuracy but paid less attention to p...

A Deep Learning Radiomics Model to Identify Poor Outcome in COVID-19 Patients With Underlying Health Conditions: A Multicenter Study.

IEEE journal of biomedical and health informatics
OBJECTIVE: Coronavirus disease 2019 (COVID-19) has caused considerable morbidity and mortality, especially in patients with underlying health conditions. A precise prognostic tool to identify poor outcomes among such cases is desperately needed.

COVID-19 Automatic Diagnosis With Radiographic Imaging: Explainable Attention Transfer Deep Neural Networks.

IEEE journal of biomedical and health informatics
Researchers seek help from deep learning methods to alleviate the enormous burden of reading radiological images by clinicians during the COVID-19 pandemic. However, clinicians are often reluctant to trust deep models due to their black-box character...

Detailed Assessment of Sleep Architecture With Deep Learning and Shorter Epoch-to-Epoch Duration Reveals Sleep Fragmentation of Patients With Obstructive Sleep Apnea.

IEEE journal of biomedical and health informatics
Traditional sleep staging with non-overlapping 30-second epochs overlooks multiple sleep-wake transitions. We aimed to overcome this by analyzing the sleep architecture in more detail with deep learning methods and hypothesized that the traditional s...

Copula-Based Data Augmentation on a Deep Learning Architecture for Cardiac Sensor Fusion.

IEEE journal of biomedical and health informatics
In the wake of Big Data, traditional Machine Learning techniques are now often integrated in the clinical workflow. Despite more capable, Deep Learning methods are not equally accepted given their unsatiated need for great amounts of training data an...

Deep Learning for Diabetes: A Systematic Review.

IEEE journal of biomedical and health informatics
Diabetes is a chronic metabolic disorder that affects an estimated 463 million people worldwide. Aiming to improve the treatment of people with diabetes, digital health has been widely adopted in recent years and generated a huge amount of data that ...

Anthropometric Landmark Detection in 3D Head Surfaces Using a Deep Learning Approach.

IEEE journal of biomedical and health informatics
Landmark labeling in 3D head surfaces is an important and routine task in clinical practice to evaluate head shape, namely to analyze cranial deformities or growth evolution. However, manual labeling is still applied, being a tedious and time-consumi...