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Sean Benson

Showing results (11-20 of 18) with videos related to

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Cardiovascular and Interventional Radiology|September 5, 2023
CIRSE Position Paper on Artificial Intelligence in Interventional RadiologyArash Najafi, Roberto Luigi Cazzato, Bernhard C Meyer, et al.
European Radiology|January 28, 2026
Semi-supervised learning in prostate MRI tumor detection approaches fully supervised performance on external validationEduardo H P Pooch, Georgios Agrotis, Lishan Cai, et al.
Diagnostics (Basel, Switzerland)|October 14, 2023
A Deep Learning Framework with Explainability for the Prediction of Lateral Locoregional Recurrences in Rectal Cancer Patients with Suspicious Lateral Lymph NodesTania C Sluckin, Marije Hekhuis, Sabrine Q Kol, et al.
European Journal of Radiology|February 3, 2024
Multi-sequence MRI radiomics of colorectal liver metastases: Which features are reproducible across readers?Denise J van der Reijd, Kalina Chupetlovska, Eleanor van Dijk, et al.
European Radiology|November 21, 2023
Independent validation of CT radiomics models in colorectal liver metastases: predicting local tumour progression after ablationDenise J van der Reijd, Corentin Guerendel, Femke C R Staal, et al.
European Radiology Experimental|March 12, 2026
Tumor morphology on CT radiomics is largely driven by the local anatomical environment, not the primary tumor typeSajjad Rostami, Corentin Guérendel, Marleen Soliman, et al.
Nature Communications|December 5, 2022
Federated learning enables big data for rare cancer boundary detectionSarthak Pati, Ujjwal Baid, Brandon Edwards, et al.
Nature Communications|January 26, 2023
Author Correction: Federated learning enables big data for rare cancer boundary detectionSarthak Pati, Ujjwal Baid, Brandon Edwards, et al.
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Showing results (11-20 of 18) with videos related to

Sort By:
Pageof 2
You have reached the last page of results.This site can display upto 18 results.
Cardiovascular and Interventional Radiology|September 5, 2023
CIRSE Position Paper on Artificial Intelligence in Interventional RadiologyArash Najafi, Roberto Luigi Cazzato, Bernhard C Meyer, et al.
European Radiology|January 28, 2026
Semi-supervised learning in prostate MRI tumor detection approaches fully supervised performance on external validationEduardo H P Pooch, Georgios Agrotis, Lishan Cai, et al.
Diagnostics (Basel, Switzerland)|October 14, 2023
A Deep Learning Framework with Explainability for the Prediction of Lateral Locoregional Recurrences in Rectal Cancer Patients with Suspicious Lateral Lymph NodesTania C Sluckin, Marije Hekhuis, Sabrine Q Kol, et al.
European Journal of Radiology|February 3, 2024
Multi-sequence MRI radiomics of colorectal liver metastases: Which features are reproducible across readers?Denise J van der Reijd, Kalina Chupetlovska, Eleanor van Dijk, et al.
European Radiology|November 21, 2023
Independent validation of CT radiomics models in colorectal liver metastases: predicting local tumour progression after ablationDenise J van der Reijd, Corentin Guerendel, Femke C R Staal, et al.
European Radiology Experimental|March 12, 2026
Tumor morphology on CT radiomics is largely driven by the local anatomical environment, not the primary tumor typeSajjad Rostami, Corentin Guérendel, Marleen Soliman, et al.
Nature Communications|December 5, 2022
Federated learning enables big data for rare cancer boundary detectionSarthak Pati, Ujjwal Baid, Brandon Edwards, et al.
Nature Communications|January 26, 2023
Author Correction: Federated learning enables big data for rare cancer boundary detectionSarthak Pati, Ujjwal Baid, Brandon Edwards, et al.
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