Zobrazeno 1 - 10
of 1 443
pro vyhledávání: '"Samei Ehsan"'
Autor:
Dahal, Lavsen, Ghojoghnejad, Mobina, Ghosh, Dhrubajyoti, Bhandari, Yubraj, Kim, David, Ho, Fong Chi, Tushar, Fakrul Islam, Luoa, Sheng, Lafata, Kyle J., Abadi, Ehsan, Samei, Ehsan, Lo, Joseph Y., Segars, W. Paul
Virtual Imaging Trials (VIT) offer a cost-effective and scalable approach for evaluating medical imaging technologies. Computational phantoms, which mimic real patient anatomy and physiology, play a central role in VITs. However, the current librarie
Externí odkaz:
http://arxiv.org/abs/2405.11133
Autor:
Abadi, Ehsan, Badano, Aldo, Bakic, Predrag, Bliznakova, Kristina, Bosmans, Hilde, Carton, Ann-Katherine, Frangi, Alejandro, Glick, Stephen, Kinahan, Paul, Lo, Joseph, Maidment, Andrew, Ria, Francesco, Samei, Ehsan, Sechopoulos, Ioannis, Segars, Paul, Tanaka, Rie, Vancoillie, Liesbeth
This submission comprises the proceedings of the 1st Virtual Imaging Trials in Medicine conference, organized by Duke University on April 22-24, 2024. The listed authors serve as the program directors for this conference. The VITM conference is a pio
Externí odkaz:
http://arxiv.org/abs/2405.05359
Autor:
Tushar, Fakrul Islam, Vancoillie, Liesbeth, McCabe, Cindy, Kavuri, Amareswararao, Dahal, Lavsen, Harrawood, Brian, Fryling, Milo, Zarei, Mojtaba, Sotoudeh-Paima, Saman, Ho, Fong Chi, Ghosh, Dhrubajyoti, Harowicz, Michael R., Tailor, Tina D., Luo, Sheng, Segars, W. Paul, Abadi, Ehsan, Lafata, Kyle J., Lo, Joseph Y., Samei, Ehsan
Objectives: To demonstrate that a virtual imaging trial platform can accurately emulate a major clinical trial, specifically the National Lung Screening Trial (NLST) that compared computed tomography (CT) and chest radiography (CXR) imaging for lung
Externí odkaz:
http://arxiv.org/abs/2404.11221
Autor:
Mouheb, Kaouther, Nejad, Mobina Ghojogh, Dahal, Lavsen, Samei, Ehsan, Lafata, Kyle J., Segars, W. Paul, Lo, Joseph Y.
Accurate 3D modeling of human organs plays a crucial role in building computational phantoms for virtual imaging trials. However, generating anatomically plausible reconstructions of organ surfaces from computed tomography scans remains challenging f
Externí odkaz:
http://arxiv.org/abs/2309.08289
Autor:
Tushar, Fakrul Islam, Dahal, Lavsen, Sotoudeh-Paima, Saman, Abadi, Ehsan, Segars, W. Paul, Samei, Ehsan, Lo, Joseph Y.
The credibility of Artificial Intelligence (AI) models in medical imaging, particularly during the COVID-19 pandemic, has been challenged by reproducibility issues and obscured clinical insights. To address these concerns, we propose a Virtual Imagin
Externí odkaz:
http://arxiv.org/abs/2308.09730
Autor:
Tushar, Fakrul Islam, Abadi, Ehsan, Sotoudeh-Paima, Saman, Fricks, Rafael B., Mazurowski, Maciej A., Segars, W. Paul, Samei, Ehsan, Lo, Joseph Y.
Research studies of artificial intelligence models in medical imaging have been hampered by poor generalization. This problem has been especially concerning over the last year with numerous applications of deep learning for COVID-19 diagnosis. Virtua
Externí odkaz:
http://arxiv.org/abs/2203.03074
Autor:
Tushar, Fakrul Islam, Nujaim, Husam, Fu, Wanyi, Abadi, Ehsan, Mazurowski, Maciej A., Samei, Ehsan, Segars, William P., Lo, Joseph Y.
Organ segmentation of medical images is a key step in virtual imaging trials. However, organ segmentation datasets are limited in terms of quality (because labels cover only a few organs) and quantity (since case numbers are limited). In this study,
Externí odkaz:
http://arxiv.org/abs/2203.01934