Zobrazeno 1 - 10
of 47
pro vyhledávání: '"Nabavi, Shahabedin"'
Autor:
Nabavi, Shahabedin, Hamedani, Kian Anvari, Moghaddam, Mohsen Ebrahimi, Abin, Ahmad Ali, Frangi, Alejandro F.
This study proposes an attention-based statistical distance-guided unsupervised domain adaptation model for multi-class cardiovascular magnetic resonance (CMR) image quality assessment. The proposed model consists of a feature extractor, a label pred
Externí odkaz:
http://arxiv.org/abs/2409.00375
Autor:
Nabavi, Shahabedin, Hamedani, Kian Anvari, Moghaddam, Mohsen Ebrahimi, Abin, Ahmad Ali, Frangi, Alejandro F.
Background: Image classification can be considered one of the key pillars of medical image analysis. Deep learning (DL) faces challenges that prevent its practical applications despite the remarkable improvement in medical image classification. The d
Externí odkaz:
http://arxiv.org/abs/2403.11226
Autor:
Gheshlaghi, Tara, Nabavi, Shahabedin, Shirzadikia, Samire, Moghaddam, Mohsen Ebrahimi, Rostampour, Nima
Publikováno v:
Physics in Medicine & Biology, Volume 69, Number 4, 2024
Radiation therapy is the primary method used to treat cancer in the clinic. Its goal is to deliver a precise dose to the planning target volume (PTV) while protecting the surrounding organs at risk (OARs). However, the traditional workflow used by do
Externí odkaz:
http://arxiv.org/abs/2307.12005
Autor:
Nabavi, Shahabedin, Simchi, Hossein, Moghaddam, Mohsen Ebrahimi, Abin, Ahmad Ali, Frangi, Alejandro F.
Publikováno v:
Computer Methods and Programs in Biomedicine, Volume 242, 2023, 107770
Background and Objectives: Cardiovascular magnetic resonance (CMR) imaging is a powerful modality in functional and anatomical assessment for various cardiovascular diseases. Sufficient image quality is essential to achieve proper diagnosis and treat
Externí odkaz:
http://arxiv.org/abs/2303.13324
Publikováno v:
Multimedia Tools and Applications, Volume 83, pages 12209-12233, 2024
Recently, the attention-enriched encoder-decoder framework has aroused great interest in image captioning due to its overwhelming progress. Many visual attention models directly leverage meaningful regions to generate image descriptions. However, see
Externí odkaz:
http://arxiv.org/abs/2302.04676
Autor:
Nabavi, Shahabedin, Hashemi, Mohammad, Moghaddam, Mohsen Ebrahimi, Abin, Ahmad Ali, Frangi, Alejandro F.
Publikováno v:
Medical Physics, 2024
Cardiovascular magnetic resonance (CMR) imaging has become a modality with superior power for the diagnosis and prognosis of cardiovascular diseases. One of the essential basic quality controls of CMR images is to investigate the complete cardiac cov
Externí odkaz:
http://arxiv.org/abs/2206.06844
Autor:
Nabavi, Shahabedin, Simchi, Hossein, Moghaddam, Mohsen Ebrahimi, Frangi, Alejandro F., Abin, Ahmad Ali
Population imaging studies rely upon good quality medical imagery before downstream image quantification. This study provides an automated approach to assess image quality from cardiovascular magnetic resonance (CMR) imaging at scale. We identify fou
Externí odkaz:
http://arxiv.org/abs/2112.06806
Autor:
Nabavi, Shahabedin, Ejmalian, Azar, Moghaddam, Mohsen Ebrahimi, Abin, Ahmad Ali, Frangi, Alejandro F., Mohammadi, Mohammad, Rad, Hamidreza Saligheh
Publikováno v:
Computers in Biology and Medicine, 2021, 104605
Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. The disease presents with symptoms such as shortness of breath, fever, dry cough, and chronic fatigue, amongst others. Sometimes the symptoms of the dis
Externí odkaz:
http://arxiv.org/abs/2010.02154
Akademický článek
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Publikováno v:
Aircraft Engineering and Aerospace Technology, 2021, Vol. 93, Issue 10, pp. 1664-1673.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/AEAT-10-2020-0238
