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Tal Zeevi

Showing results (21-30 of 39) with videos related to

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Frontiers in Neuroscience|September 1, 2023
Radiomic markers of intracerebral hemorrhage expansion on non-contrast CT: independent validation and comparison with visual markersStefan P Haider, Adnan I Qureshi, Abhi Jain, et al.
AJNR. American Journal of Neuroradiology|May 16, 2025
FDG-PET intensity normalization improves radiomics- based survival prediction in oropharyngeal cancer patients: a comparison of the SUV with alternative normalization techniquesSeyedmehdi Payabvash, Kariem Sharaf, Tal Zeevi, et al.
Frontiers in Artificial Intelligence|August 16, 2024
Deep learning for prediction of post-thrombectomy outcomes based on admission CT angiography in large vessel occlusion strokeJakob Sommer, Fiona Dierksen, Tal Zeevi, et al.
Applied Sciences (Basel, Switzerland)|March 6, 2025
Optimizing Automated Hematoma Expansion Classification from Baseline and Follow-Up Head Computed TomographyAnh T Tran, Dmitriy Desser, Tal Zeevi, et al.
Frontiers in Neuroscience|October 31, 2022
Clinical implementation of artificial intelligence in neuroradiology with development of a novel workflow-efficient picture archiving and communication system-based automated brain tumor segmentation and radiomic feature extractionMariam Aboian, Khaled Bousabarah, Eve Kazarian, et al.
AJR. American Journal of Roentgenology|August 17, 2022
Machine Learning Models for Prediction of Posttreatment Recurrence in Early-Stage Hepatocellular Carcinoma Using Pretreatment Clinical and MRI Features: A Proof-of-Concept StudySimon Iseke, Tal Zeevi, Ahmet S Kucukkaya, et al.
NPJ Digital Medicine|February 6, 2024
Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scanAnh T Tran, Tal Zeevi, Stefan P Haider, et al.
Scientific Reports|May 10, 2023
Predicting tumor recurrence on baseline MR imaging in patients with early-stage hepatocellular carcinoma using deep machine learningAhmet Said Kucukkaya, Tal Zeevi, Nathan Xianming Chai, et al.
European Journal of Neurology|June 30, 2021
Admission computed tomography radiomic signatures outperform hematoma volume in predicting baseline clinical severity and functional outcome in the ATACH-2 trial intracerebral hemorrhage populationStefan P Haider, Adnan I Qureshi, Abhi Jain, et al.
Frontiers in Oncology|May 9, 2022
Machine Learning Models for Classifying High- and Low-Grade Gliomas: A Systematic Review and Quality of Reporting AnalysisRyan C Bahar, Sara Merkaj, Gabriel I Cassinelli Petersen, et al.
Pageof 4

Showing results (21-30 of 39) with videos related to

Sort By:
Pageof 4
Frontiers in Neuroscience|September 1, 2023
Radiomic markers of intracerebral hemorrhage expansion on non-contrast CT: independent validation and comparison with visual markersStefan P Haider, Adnan I Qureshi, Abhi Jain, et al.
AJNR. American Journal of Neuroradiology|May 16, 2025
FDG-PET intensity normalization improves radiomics- based survival prediction in oropharyngeal cancer patients: a comparison of the SUV with alternative normalization techniquesSeyedmehdi Payabvash, Kariem Sharaf, Tal Zeevi, et al.
Frontiers in Artificial Intelligence|August 16, 2024
Deep learning for prediction of post-thrombectomy outcomes based on admission CT angiography in large vessel occlusion strokeJakob Sommer, Fiona Dierksen, Tal Zeevi, et al.
Applied Sciences (Basel, Switzerland)|March 6, 2025
Optimizing Automated Hematoma Expansion Classification from Baseline and Follow-Up Head Computed TomographyAnh T Tran, Dmitriy Desser, Tal Zeevi, et al.
Frontiers in Neuroscience|October 31, 2022
Clinical implementation of artificial intelligence in neuroradiology with development of a novel workflow-efficient picture archiving and communication system-based automated brain tumor segmentation and radiomic feature extractionMariam Aboian, Khaled Bousabarah, Eve Kazarian, et al.
AJR. American Journal of Roentgenology|August 17, 2022
Machine Learning Models for Prediction of Posttreatment Recurrence in Early-Stage Hepatocellular Carcinoma Using Pretreatment Clinical and MRI Features: A Proof-of-Concept StudySimon Iseke, Tal Zeevi, Ahmet S Kucukkaya, et al.
NPJ Digital Medicine|February 6, 2024
Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scanAnh T Tran, Tal Zeevi, Stefan P Haider, et al.
Scientific Reports|May 10, 2023
Predicting tumor recurrence on baseline MR imaging in patients with early-stage hepatocellular carcinoma using deep machine learningAhmet Said Kucukkaya, Tal Zeevi, Nathan Xianming Chai, et al.
European Journal of Neurology|June 30, 2021
Admission computed tomography radiomic signatures outperform hematoma volume in predicting baseline clinical severity and functional outcome in the ATACH-2 trial intracerebral hemorrhage populationStefan P Haider, Adnan I Qureshi, Abhi Jain, et al.
Frontiers in Oncology|May 9, 2022
Machine Learning Models for Classifying High- and Low-Grade Gliomas: A Systematic Review and Quality of Reporting AnalysisRyan C Bahar, Sara Merkaj, Gabriel I Cassinelli Petersen, et al.
Pageof 4