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Christopher P Bridge

Showing results (11-20 of 42) with videos related to

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Cardiovascular and Interventional Radiology|March 31, 2026
Retrieval-Augmented Language Models for Patient-Centered Periprocedural Anticoagulation in Interventional RadiologyHossam A Zaki, Allison Brea, Krishnaveni Parvataneni, et al.
Cancers|June 19, 2024
CASCADE: Context-Aware Data-Driven AI for Streamlined Multidisciplinary Tumor Board Recommendations in OncologyDania Daye, Regina Parker, Satvik Tripathi, et al.
Physics and Imaging in Radiation Oncology|January 27, 2025
Investigating the potential of diffusion tensor atlases to generate anisotropic clinical tumor volumes in glioblastoma patientsKim Hochreuter, Gregory Buti, Ali Ajdari, et al.
Physics and Imaging in Radiation Oncology|December 2, 2025
Clinical target volumes for glioma - Automated delineation to improve neuroanatomic consistencyGregory Buti, Marcela Giovenco, Tugba Yilmaz, et al.
Physics and Imaging in Radiation Oncology|May 6, 2026
Corrigendum to "Clinical target volumes for glioma - Automated delineation to improve neuroanatomic consistency" [Phys. Imaging Radiat. Oncol. 36 (2025) 100865]Gregory Buti, Marcela Giovenco, Tugba Yilmaz, et al.
Scientific Data|January 4, 2024
Enrichment of lung cancer computed tomography collections with AI-derived annotationsDeepa Krishnaswamy, Dennis Bontempi, Vamsi Krishna Thiriveedhi, et al.
NPJ Digital Medicine|November 18, 2022
Improving the repeatability of deep learning models with Monte Carlo dropoutAndreanne Lemay, Katharina Hoebel, Christopher P Bridge, et al.
Clinical Imaging|June 5, 2024
No code machine learning: validating the approach on use-case for classifying clavicle fracturesGiridhar Dasegowda, James Yuichi Sato, Daniel C Elton, et al.
Journal of Digital Imaging|August 22, 2022
Highdicom: a Python Library for Standardized Encoding of Image Annotations and Machine Learning Model Outputs in Pathology and RadiologyChristopher P Bridge, Chris Gorman, Steven Pieper, et al.
Abdominal Radiology (New York)|March 26, 2025
Using interpretable rule-learning artificial intelligence to optimally differentiate adrenal pheochromocytomas from adenomas with CT radiomicsDaniel I Glazer, Melissa Viator, Andrew Sharp, et al.
Pageof 5

Showing results (11-20 of 42) with videos related to

Sort By:
Pageof 5
Cardiovascular and Interventional Radiology|March 31, 2026
Retrieval-Augmented Language Models for Patient-Centered Periprocedural Anticoagulation in Interventional RadiologyHossam A Zaki, Allison Brea, Krishnaveni Parvataneni, et al.
Cancers|June 19, 2024
CASCADE: Context-Aware Data-Driven AI for Streamlined Multidisciplinary Tumor Board Recommendations in OncologyDania Daye, Regina Parker, Satvik Tripathi, et al.
Physics and Imaging in Radiation Oncology|January 27, 2025
Investigating the potential of diffusion tensor atlases to generate anisotropic clinical tumor volumes in glioblastoma patientsKim Hochreuter, Gregory Buti, Ali Ajdari, et al.
Physics and Imaging in Radiation Oncology|December 2, 2025
Clinical target volumes for glioma - Automated delineation to improve neuroanatomic consistencyGregory Buti, Marcela Giovenco, Tugba Yilmaz, et al.
Physics and Imaging in Radiation Oncology|May 6, 2026
Corrigendum to "Clinical target volumes for glioma - Automated delineation to improve neuroanatomic consistency" [Phys. Imaging Radiat. Oncol. 36 (2025) 100865]Gregory Buti, Marcela Giovenco, Tugba Yilmaz, et al.
Scientific Data|January 4, 2024
Enrichment of lung cancer computed tomography collections with AI-derived annotationsDeepa Krishnaswamy, Dennis Bontempi, Vamsi Krishna Thiriveedhi, et al.
NPJ Digital Medicine|November 18, 2022
Improving the repeatability of deep learning models with Monte Carlo dropoutAndreanne Lemay, Katharina Hoebel, Christopher P Bridge, et al.
Clinical Imaging|June 5, 2024
No code machine learning: validating the approach on use-case for classifying clavicle fracturesGiridhar Dasegowda, James Yuichi Sato, Daniel C Elton, et al.
Journal of Digital Imaging|August 22, 2022
Highdicom: a Python Library for Standardized Encoding of Image Annotations and Machine Learning Model Outputs in Pathology and RadiologyChristopher P Bridge, Chris Gorman, Steven Pieper, et al.
Abdominal Radiology (New York)|March 26, 2025
Using interpretable rule-learning artificial intelligence to optimally differentiate adrenal pheochromocytomas from adenomas with CT radiomicsDaniel I Glazer, Melissa Viator, Andrew Sharp, et al.
Pageof 5