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Darvin Yi

Showing results (21-30 of 29) with videos related to

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Translational Vision Science & Technology|November 3, 2023
Validating the Generalizability of Ophthalmic Artificial Intelligence Models on Real-World Clinical DataHoma Rashidisabet, Abhishek Sethi, Ponpawee Jindarak, et al.
Medical Physics|August 18, 2021
MRI pulse sequence integration for deep-learning-based brain metastases segmentationDarvin Yi, Endre Grøvik, Elizabeth Tong, et al.
Medical Physics|August 13, 2023
Erratum: "MRI pulse sequence integration for deep-learning-based brain metastases segmentation"Darvin Yi, Endre Grøvik, Elizabeth Tong, et al.
NPJ Digital Medicine|February 23, 2021
Handling missing MRI sequences in deep learning segmentation of brain metastases: a multicenter studyEndre Grøvik, Darvin Yi, Michael Iv, et al.
Frontiers in Neuroinformatics|February 6, 2023
2.5D and 3D segmentation of brain metastases with deep learning on multinational MRI dataJon André Ottesen, Darvin Yi, Elizabeth Tong, et al.
Ophthalmology Science|September 11, 2025
Open-Source Periorbital Segmentation Dataset for Ophthalmic ApplicationsGeorge R Nahass, Emma Koehler, Nicholas Tomaras, et al.
Ophthalmology Science|September 8, 2025
Development and Validation of a Semiautomated Tool for Measuring Periorbital DistancesJeffrey C Peterson, George R Nahass, Claudia Lasalle, et al.
JMIR Human Factors|April 30, 2026
Simulated Workflow Feasibility Evaluation of a Web-Based Periorbital Measurement Platform: Development and Usability StudyGeorge R Nahass, Jacob van der Ende, Sasha Hubschman, et al.
JAMA Network Open|March 3, 2020
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening MammogramsThomas Schaffter, Diana S M Buist, Christoph I Lee, et al.
Pageof 3

Showing results (21-30 of 29) with videos related to

Sort By:
Pageof 3
You have reached the last page of results.This site can display upto 29 results.
Translational Vision Science & Technology|November 3, 2023
Validating the Generalizability of Ophthalmic Artificial Intelligence Models on Real-World Clinical DataHoma Rashidisabet, Abhishek Sethi, Ponpawee Jindarak, et al.
Medical Physics|August 18, 2021
MRI pulse sequence integration for deep-learning-based brain metastases segmentationDarvin Yi, Endre Grøvik, Elizabeth Tong, et al.
Medical Physics|August 13, 2023
Erratum: "MRI pulse sequence integration for deep-learning-based brain metastases segmentation"Darvin Yi, Endre Grøvik, Elizabeth Tong, et al.
NPJ Digital Medicine|February 23, 2021
Handling missing MRI sequences in deep learning segmentation of brain metastases: a multicenter studyEndre Grøvik, Darvin Yi, Michael Iv, et al.
Frontiers in Neuroinformatics|February 6, 2023
2.5D and 3D segmentation of brain metastases with deep learning on multinational MRI dataJon André Ottesen, Darvin Yi, Elizabeth Tong, et al.
Ophthalmology Science|September 11, 2025
Open-Source Periorbital Segmentation Dataset for Ophthalmic ApplicationsGeorge R Nahass, Emma Koehler, Nicholas Tomaras, et al.
Ophthalmology Science|September 8, 2025
Development and Validation of a Semiautomated Tool for Measuring Periorbital DistancesJeffrey C Peterson, George R Nahass, Claudia Lasalle, et al.
JMIR Human Factors|April 30, 2026
Simulated Workflow Feasibility Evaluation of a Web-Based Periorbital Measurement Platform: Development and Usability StudyGeorge R Nahass, Jacob van der Ende, Sasha Hubschman, et al.
JAMA Network Open|March 3, 2020
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening MammogramsThomas Schaffter, Diana S M Buist, Christoph I Lee, et al.
Pageof 3