A
Azadeh Akhavanallaf
Researcher at Geneva College
Publications - 22
Citations - 281
Azadeh Akhavanallaf is an academic researcher from Geneva College. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 5, co-authored 14 publications receiving 96 citations. Previous affiliations of Azadeh Akhavanallaf include University of Geneva.
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The promise of artificial intelligence and deep learning in PET and SPECT imaging.
TL;DR: In this article, the authors discuss the foremost applications of artificial intelligence (AI), particularly deep learning (DL) algorithms, in single-photon emission computed tomography (SPECT) and positron emission tomography(PET) imaging.
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Ultra-low-dose chest CT imaging of COVID-19 patients using a deep residual neural network
Isaac Shiri,Azadeh Akhavanallaf,Amirhossein Sanaat,Yazdan Salimi,Dariush Askari,Zahra Mansouri,Sajad P. Shayesteh,Mohammad Hasanian,Kiara Rezaei-Kalantari,Ali Salahshour,Saleh Sandoughdaran,Hamid Abdollahi,Hossein Arabi,Habib Zaidi,Habib Zaidi +14 more
TL;DR: The results demonstrated that the deep learning algorithm is capable of predicting standard full-dose CT images with acceptable quality for the clinical diagnosis of COVID-19 positive patients with substantial radiation dose reduction.
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Whole-body voxel-based internal dosimetry using deep learning
TL;DR: The proposed DNN-based WB internal Dosimetry exhibited comparable performance to the direct Monte Carlo approach while overcoming the limitations of conventional dosimetry techniques in nuclear medicine.
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Development of a Library of Adult Computational Phantoms Based on Anthropometric Indexes
TL;DR: The development of an adult computational anthropomorphic phantom library covering different body morphometries among the 20–80 years old population is reported on, fulfilling the criteria for representing a diverse adult population with different anthropomorphic and anatomical characteristics.
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COLI-Net: Deep learning-assisted fully automated COVID-19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images
Isaac Shiri,Hossein Arabi,Yazdan Salimi,Amirhossein Sanaat,Azadeh Akhavanallaf,Ghasem Hajianfar,Dariush Askari,Shakiba Moradi,Zahra Mansouri,Masoumeh Pakbin,Saleh Sandoughdaran,Hamid Abdollahi,Amir Reza Radmard,Kiara Rezaei-Kalantari,Mostafa Ghelich Oghli,Habib Zaidi +15 more
TL;DR: In this paper, a deep learning-based automated whole lung and COVID-19 pneumonia infectious lesions (COLI-Net) detection and segmentation from chest computed tomography (CT) images was presented.