76 citations · 119 across the 16 of their papers we have counts for
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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…
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…