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Objective Evaluation of Deep Uncertainty Predictions for COVID-19 Detection
Hamzeh Asgharnezhad, Afshar Shamsi, Roohallah Alizadehsani +4
Deep neural networks (DNNs) have been widely applied for detecting COVID-19 in medical images. Existing studies mainly apply transfer learning and other data representation strateg…
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
Moloud Abdar, Farhad Pourpanah, Sadiq Hussain +9
Uncertainty quantification (UQ) plays a pivotal role in reduction of uncertainties during both optimization and decision making processes. It can be applied to solve a variety of r…
Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991-2020)
Roohallah Alizadehsani, Mohamad Roshanzamir, Sadiq Hussain +12
Understanding data and reaching valid conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have widespread applic…
Development of novel algorithm to visualize blood vessels on 3D ultrasound images during liver surgery
Fatemeh Salehihafshejani, Alireza Ahmadian, Afshin Shoeibi +5
Volume visualization is a method that displays three-dimensional (3D) data in two-dimensional (2D) space. Using 3D datasets instead of 2D traditional images improves the visualizat…
An Uncertainty-aware Transfer Learning-based Framework for Covid-19 Diagnosis
Afshar Shamsi Jokandan, Hamzeh Asgharnezhad, Shirin Shamsi Jokandan +5
The early and reliable detection of COVID-19 infected patients is essential to prevent and limit its outbreak. The PCR tests for COVID-19 detection are not available in many countr…
Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review
Afshin Shoeibi, Marjane Khodatars, Mahboobeh Jafari +12
Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around the world by directly affecting the lungs. COVID-19 is a medium-sized, coated…
SpinalNet: Deep Neural Network with Gradual Input
H M Dipu Kabir, Moloud Abdar, Seyed Mohammad Jafar Jalali +4
Deep neural networks (DNNs) have achieved the state of the art performance in numerous fields. However, DNNs need high computation times, and people always expect better performanc…
Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review
Marjane Khodatars, Afshin Shoeibi, Delaram Sadeghi +12
Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) techniques…
Epileptic Seizures Detection Using Deep Learning Techniques: A Review
Afshin Shoeibi, Marjane Khodatars, Navid Ghassemi +14
A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial…
Material Recognition for Automated Progress Monitoring using Deep Learning Methods
Hadi Mahami, Navid Ghassemi, Mohammad Tayarani Darbandy +7
Recent advancements in Artificial intelligence, especially deep learning, has changed many fields irreversibly by introducing state of the art methods for automation. Construction…