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Tal Zeevi

Showing results (31-40 of 39) with videos related to

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Diagnostics (Basel, Switzerland)|March 13, 2024
Radiomics-Based Prediction of Collateral Status from CT Angiography of Patients Following a Large Vessel Occlusion StrokeEmily W Avery, Anthony Abou-Karam, Sandra Abi-Fadel, et al.
European Urology Oncology|February 9, 2025
Comparative Performance of Machine Learning Models in Reducing Unnecessary Targeted Prostate BiopsiesFuyao Chen, Roxana Esmaili, Ghazal Khajir, et al.
Radiology|August 5, 2025
Noninvasive Tumor Profiling: Quantitative Contrast-Enhanced MRI Markers Predict PD-L1 and CTNNB1 Status in Hepatocellular CarcinomaNickolai J Matuschewski, Rabea Sobirey, Margarita Revzin, et al.
Bioengineering (Basel, Switzerland)|January 8, 2025
A Hybrid Transformer-Convolutional Neural Network for Segmentation of Intracerebral Hemorrhage and Perihematomal Edema on Non-Contrast Head Computed Tomography (CT) with Uncertainty Quantification to Improve ConfidenceAnh T Tran, Dmitriy Desser, Tal Zeevi, et al.
Neuroimage. Clinical|May 13, 2022
CT angiographic radiomics signature for risk stratification in anterior large vessel occlusion strokeEmily W Avery, Jonas Behland, Adrian Mak, et al.
Journal of Vascular and Interventional Radiology : JVIR|April 23, 2022
MR Imaging Biomarkers for the Prediction of Outcome after Radiofrequency Ablation of Hepatocellular Carcinoma: Qualitative and Quantitative Assessments of the Liver Imaging Reporting and Data System and Radiomic FeaturesAlexandra Petukhova-Greenstein, Tal Zeevi, Junlin Yang, et al.
Data in Brief|September 5, 2022
Dataset on acute stroke risk stratification from CT angiographic radiomicsEmily W Avery, Jonas Behland, Adrian Mak, et al.
Cancers|March 25, 2022
Machine Learning Applications for Differentiation of Glioma from Brain Metastasis-A Systematic ReviewLeon Jekel, Waverly R Brim, Marc von Reppert, et al.
Neuro-Oncology Advances|September 8, 2022
Identifying clinically applicable machine learning algorithms for glioma segmentation: recent advances and discoveriesNiklas Tillmanns, Avery E Lum, Gabriel Cassinelli, et al.
Pageof 4

Showing results (31-40 of 39) with videos related to

Sort By:
Pageof 4
You have reached the last page of results.This site can display upto 39 results.
Diagnostics (Basel, Switzerland)|March 13, 2024
Radiomics-Based Prediction of Collateral Status from CT Angiography of Patients Following a Large Vessel Occlusion StrokeEmily W Avery, Anthony Abou-Karam, Sandra Abi-Fadel, et al.
European Urology Oncology|February 9, 2025
Comparative Performance of Machine Learning Models in Reducing Unnecessary Targeted Prostate BiopsiesFuyao Chen, Roxana Esmaili, Ghazal Khajir, et al.
Radiology|August 5, 2025
Noninvasive Tumor Profiling: Quantitative Contrast-Enhanced MRI Markers Predict PD-L1 and CTNNB1 Status in Hepatocellular CarcinomaNickolai J Matuschewski, Rabea Sobirey, Margarita Revzin, et al.
Bioengineering (Basel, Switzerland)|January 8, 2025
A Hybrid Transformer-Convolutional Neural Network for Segmentation of Intracerebral Hemorrhage and Perihematomal Edema on Non-Contrast Head Computed Tomography (CT) with Uncertainty Quantification to Improve ConfidenceAnh T Tran, Dmitriy Desser, Tal Zeevi, et al.
Neuroimage. Clinical|May 13, 2022
CT angiographic radiomics signature for risk stratification in anterior large vessel occlusion strokeEmily W Avery, Jonas Behland, Adrian Mak, et al.
Journal of Vascular and Interventional Radiology : JVIR|April 23, 2022
MR Imaging Biomarkers for the Prediction of Outcome after Radiofrequency Ablation of Hepatocellular Carcinoma: Qualitative and Quantitative Assessments of the Liver Imaging Reporting and Data System and Radiomic FeaturesAlexandra Petukhova-Greenstein, Tal Zeevi, Junlin Yang, et al.
Data in Brief|September 5, 2022
Dataset on acute stroke risk stratification from CT angiographic radiomicsEmily W Avery, Jonas Behland, Adrian Mak, et al.
Cancers|March 25, 2022
Machine Learning Applications for Differentiation of Glioma from Brain Metastasis-A Systematic ReviewLeon Jekel, Waverly R Brim, Marc von Reppert, et al.
Neuro-Oncology Advances|September 8, 2022
Identifying clinically applicable machine learning algorithms for glioma segmentation: recent advances and discoveriesNiklas Tillmanns, Avery E Lum, Gabriel Cassinelli, et al.
Pageof 4