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pro vyhledávání: '"Pascal, N."'
Current methods for performing 3D reconstruction and novel view synthesis (NVS) in ultrasound imaging data often face severe artifacts when training NeRF-based approaches. The artifacts produced by current approaches differ from NeRF floaters in gene
Externí odkaz:
http://arxiv.org/abs/2408.10258
Publikováno v:
BioMedInformatics, Vol 4, Iss 3, Pp 1934-1948 (2024)
Background: Machine learning models can provide quick and reliable assessments in place of medical practitioners. With over 50 million adults in the United States suffering from osteoarthritis, there is a need for models capable of interpreting muscu
Externí odkaz:
https://doaj.org/article/cc4feb8559cb4a6f981753dc0d52511c
Autor:
Gabriel Solana-Lavalle, Michael D. Cusimano, Thomas Steeves, Roberto Rosas-Romero, Pascal N. Tyrrell
Publikováno v:
Tomography, Vol 10, Iss 6, Pp 894-911 (2024)
In recent years, Artificial Intelligence has been used to assist healthcare professionals in detecting and diagnosing neurodegenerative diseases. In this study, we propose a methodology to analyze functional Magnetic Resonance Imaging signals and per
Externí odkaz:
https://doaj.org/article/71ca4be3a2cf4f119f45874657b34c1c
Autor:
Alameen Damer, Emaan Chaudry, Daniel Eftekhari, Susanne M. Benseler, Frozan Safi, Richard I. Aviv, Pascal N. Tyrrell
Publikováno v:
Tomography, Vol 9, Iss 5, Pp 1811-1828 (2023)
Neuroimaging has a key role in identifying small-vessel vasculitis from common diseases it mimics, such as multiple sclerosis. Oftentimes, a multitude of these conditions present similarly, and thus diagnosis is difficult. To date, there is no standa
Externí odkaz:
https://doaj.org/article/6b2a95d6d7b842cd946b2acdd3b328b6
Publikováno v:
In CJC Pediatric and Congenital Heart Disease April 2024 3(2):74-78
Autor:
Pelzl, Lisann, Uzun, Günalp, Marini, Irene, Zlamal, Jan, Trumpp, Pascal N., Karakuyu, Aleyna, Bakchoul, Tamam, Althaus, Karina
Publikováno v:
In Journal of Thrombosis and Haemostasis February 2024 22(2):470-479
Publikováno v:
Tomography, Vol 9, Iss 4, Pp 1443-1455 (2023)
Objectives: This scoping review was conducted to determine the barriers and enablers associated with the acceptance of artificial intelligence/machine learning (AI/ML)-enabled innovations into radiology practice from a physician’s perspective. Meth
Externí odkaz:
https://doaj.org/article/4a0f2ad4e3344e3f9c8701d30772489b
Publikováno v:
Tomography, Vol 9, Iss 3, Pp 901-908 (2023)
Background: Training machine learning (ML) models in medical imaging requires large amounts of labeled data. To minimize labeling workload, it is common to divide training data among multiple readers for separate annotation without consensus and then
Externí odkaz:
https://doaj.org/article/5e381e57514b42a48040a7ee9a36b1ca
Autor:
Felipe Sanchez, Pascal N. Tyrrell, Patrick Cheung, Chinthaka Heyn, Simon Graham, Ian Poon, Yee Ung, Alexander Louie, May Tsao, Anastasia Oikonomou
Publikováno v:
Cancer Imaging, Vol 23, Iss 1, Pp 1-9 (2023)
Abstract Background Although MRI is a radiation-free imaging modality, it has historically been limited in lung imaging due to inherent technical restrictions. The aim of this study is to explore the performance of lung MRI in detecting solid and sub
Externí odkaz:
https://doaj.org/article/ddc40f64c391435dbd3cf520ee84dda6
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