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P Bridge

Showing results (91-100 of 120) with videos related to

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Psychiatry Research|February 1, 1981
A probable neuroleptic effect on platelet monoamine oxidase in chronic schizophrenic patientsL E DeLisi, C D Wise, T 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.
Psychiatry Research|June 1, 1981
A biochemical study of tardive dyskinesia in young male patientsD V Jeste, L E DeLisi, S Zalcman, 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.
The Journal of Clinical Psychiatry|February 1, 1981
Lymphocyte monoamine oxidase and plasma prolactin and growth hormone in tardive dyskinesiaD V Jeste, L M Neckers, R L Wagner, et al.
Radiography (London, England : 1995)|February 14, 2020
Simulated versus traditional therapeutic radiography placements: A randomised controlled trialS-J Ketterer, J Callender, M Warren, et al.
AJR. American Journal of Roentgenology|August 31, 2022
Utility of Normalized Body Composition Areas, Derived From Outpatient Abdominal CT Using a Fully Automated Deep Learning Method, for Predicting Subsequent Cardiovascular EventsKirti Magudia, Christopher P Bridge, Camden P Bay, et al.
PLOS Digital Health|April 17, 2026
Leveraging deep learning to infer continuous predictions from ordinal labels in medical imagingKatharina V Hoebel, Andréanne Lemay, John Peter Campbell, et al.
Radiology. Artificial Intelligence|February 11, 2022
A Fully Automated Deep Learning Pipeline for Multi-Vertebral Level Quantification and Characterization of Muscle and Adipose Tissue on Chest CT ScansChristopher P Bridge, Till D Best, Maria M Wrobel, et al.
Pageof 12

Showing results (91-100 of 120) with videos related to

Sort By:
Pageof 12
Psychiatry Research|February 1, 1981
A probable neuroleptic effect on platelet monoamine oxidase in chronic schizophrenic patientsL E DeLisi, C D Wise, T 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.
Psychiatry Research|June 1, 1981
A biochemical study of tardive dyskinesia in young male patientsD V Jeste, L E DeLisi, S Zalcman, 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.
The Journal of Clinical Psychiatry|February 1, 1981
Lymphocyte monoamine oxidase and plasma prolactin and growth hormone in tardive dyskinesiaD V Jeste, L M Neckers, R L Wagner, et al.
Radiography (London, England : 1995)|February 14, 2020
Simulated versus traditional therapeutic radiography placements: A randomised controlled trialS-J Ketterer, J Callender, M Warren, et al.
AJR. American Journal of Roentgenology|August 31, 2022
Utility of Normalized Body Composition Areas, Derived From Outpatient Abdominal CT Using a Fully Automated Deep Learning Method, for Predicting Subsequent Cardiovascular EventsKirti Magudia, Christopher P Bridge, Camden P Bay, et al.
PLOS Digital Health|April 17, 2026
Leveraging deep learning to infer continuous predictions from ordinal labels in medical imagingKatharina V Hoebel, Andréanne Lemay, John Peter Campbell, et al.
Radiology. Artificial Intelligence|February 11, 2022
A Fully Automated Deep Learning Pipeline for Multi-Vertebral Level Quantification and Characterization of Muscle and Adipose Tissue on Chest CT ScansChristopher P Bridge, Till D Best, Maria M Wrobel, et al.
Pageof 12