R
Rahele Kafieh
Researcher at Isfahan University of Medical Sciences
Publications - 51
Citations - 1247
Rahele Kafieh is an academic researcher from Isfahan University of Medical Sciences. The author has contributed to research in topics: Optical coherence tomography & Segmentation. The author has an hindex of 10, co-authored 51 publications receiving 574 citations. Previous affiliations of Rahele Kafieh include Max Delbrück Center for Molecular Medicine & Newcastle University.
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Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning.
TL;DR: Inspired by earlier works, the application of deep learning models to detect COVID-19 patients from their chest radiography images and shows that the generated heatmaps contain most of the infected areas annotated by the authors' board certified radiologist.
Journal ArticleDOI
COVID TV-UNet: Segmenting COVID-19 Chest CT Images Using Connectivity Imposed U-Net
TL;DR: TV-UNet as mentioned in this paper uses an architecture similar to U-Net model, and trains it to detect ground glass regions, on pixel level, to promote connectivity of the segmentation map for COVID-19 pixels.
Journal ArticleDOI
COVID-19 in Iran: Forecasting Pandemic Using Deep Learning.
Rahele Kafieh,Roya Arian,Narges Saeedizadeh,Zahra Amini,Nasim Dadashi Serej,Shervin Minaee,Sunil Kumar Yadav,Atefeh Vaezi,Nima Rezaei,Shaghayegh Haghjooy Javanmard +9 more
TL;DR: In this article, the authors applied selected deep learning models including multilayer perceptron, random forest, and different versions of long shortterm memory (LSTM), using three data sources to train the models, including COVID-19 occurrences, basic information like coded country names, and detailed information like population, and area of different countries.
Posted Content
COVID TV-UNet: Segmenting COVID-19 Chest CT Images Using Connectivity Imposed U-Net
TL;DR: A segmentation framework to detect chest regions in CT images, which are infected by COVID-19, is proposed, called ”TV-Unet” because the infected regions tend to form connected components rather than randomly distributed voxels.
Journal ArticleDOI
Retinal Optical Coherence Tomography in Neuromyelitis Optica.
Frederike C. Oertel,Svenja Specovius,Hanna Zimmermann,Claudia Chien,Seyedamirhosein Motamedi,Charlotte Bereuter,Lawrence Cook,Marco Aurélio Lana Peixoto,Mariana Andrade Fontanelle,Ho Jin Kim,Jae-Won Hyun,Jacqueline Palace,Adriana Roca-Fernandez,Maria Isabel Leite,Srilakshmi M Sharma,Fereshteh Ashtari,Rahele Kafieh,Alireza Dehghani,Mohsen Pourazizi,Lekha Pandit,Anitha D'Cunha,Orhan Aktas,Marius Ringelstein,Philipp Albrecht,Eugene May,Caryl Tongco,Letizia Leocani,Marco Pisa,M. Radaelli,Elena H. Martinez-Lapiscina,Hadas Stiebel-Kalish,Sasitorn Siritho,Jérôme De Seze,Thomas Senger,Joachim Havla,Romain Marignier,Alvaro Cobo Calvo,Denis Bernardi Bichuetti,Ivan Maynart Tavares,Nasrin Asgari,Kerstin Soelberg,Ayse Altintas,Rengin Yildirim,Uygur Tanriverdi,Anu Jacob,Saif Huda,Zoe Rimler,Allyson Reid,Yang Mao-Draayer,Ibis Soto de Castillo,Axel Petzold,Ari J. Green,Michael R. Yeaman,Terry J. Smith,Alexander U. Brandt,Friedemann Paul,Friedemann Paul,Friedemann Paul +57 more
TL;DR: In this paper, a large-scale Collaborative Retrospective Study on retinal optical coherence tomography (OCT) in neuromyelitis optica spectrum disorders (NMOSD) was conducted.