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George Shih

Showing results (71-80 of 94) with videos related to

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Radiology Advances|October 8, 2025
Deep learning-based liver cyst segmentation in MRI for autosomal dominant polycystic kidney diseaseMina Chookhachizadeh Moghadam, Mohit Aspal, Xinzi He, et al.
Radiology. Artificial Intelligence|March 13, 2024
Lessons Learned in Building Expertly Annotated Multi-Institution Datasets and Hosting the RSNA AI ChallengesFelipe C Kitamura, Luciano M Prevedello, Errol Colak, et al.
Radiology|November 28, 2018
The RSNA Pediatric Bone Age Machine Learning ChallengeSafwan S Halabi, Luciano M Prevedello, Jayashree Kalpathy-Cramer, et al.
Journal of Imaging Informatics in Medicine|June 4, 2025
Best Practices and Checklist for Reviewing Artificial Intelligence-Based Medical Imaging Papers: ClassificationTimothy L Kline, Felipe Kitamura, Daniel Warren, et al.
Radiology. Artificial Intelligence|May 3, 2021
Augmenting the National Institutes of Health Chest Radiograph Dataset with Expert Annotations of Possible PneumoniaGeorge Shih, Carol C Wu, Safwan S Halabi, et al.
Journal of Digital Imaging|November 27, 2019
Crowdsourcing pneumothorax annotations using machine learning annotations on the NIH chest X-ray datasetRoss W Filice, Anouk Stein, Carol C Wu, et al.
Scientific Data|October 29, 2021
CLiP, catheter and line position datasetJennifer S N Tang, Jarrel C Y Seah, Adil Zia, et al.
Scientific Data|January 29, 2021
A patient-centric dataset of images and metadata for identifying melanomas using clinical contextVeronica Rotemberg, Nicholas Kurtansky, Brigid Betz-Stablein, et al.
Scientific Data|March 6, 2021
Author Correction: A patient-centric dataset of images and metadata for identifying melanomas using clinical contextVeronica Rotemberg, Nicholas Kurtansky, Brigid Betz-Stablein, et al.
Scientific Data|March 17, 2021
Publisher Correction: Author Correction: A patient-centric dataset of images and metadata for identifying melanomas using clinical contextVeronica Rotemberg, Nicholas Kurtansky, Brigid Betz-Stablein, et al.
Pageof 10

Showing results (71-80 of 94) with videos related to

Sort By:
Pageof 10
Radiology Advances|October 8, 2025
Deep learning-based liver cyst segmentation in MRI for autosomal dominant polycystic kidney diseaseMina Chookhachizadeh Moghadam, Mohit Aspal, Xinzi He, et al.
Radiology. Artificial Intelligence|March 13, 2024
Lessons Learned in Building Expertly Annotated Multi-Institution Datasets and Hosting the RSNA AI ChallengesFelipe C Kitamura, Luciano M Prevedello, Errol Colak, et al.
Radiology|November 28, 2018
The RSNA Pediatric Bone Age Machine Learning ChallengeSafwan S Halabi, Luciano M Prevedello, Jayashree Kalpathy-Cramer, et al.
Journal of Imaging Informatics in Medicine|June 4, 2025
Best Practices and Checklist for Reviewing Artificial Intelligence-Based Medical Imaging Papers: ClassificationTimothy L Kline, Felipe Kitamura, Daniel Warren, et al.
Radiology. Artificial Intelligence|May 3, 2021
Augmenting the National Institutes of Health Chest Radiograph Dataset with Expert Annotations of Possible PneumoniaGeorge Shih, Carol C Wu, Safwan S Halabi, et al.
Journal of Digital Imaging|November 27, 2019
Crowdsourcing pneumothorax annotations using machine learning annotations on the NIH chest X-ray datasetRoss W Filice, Anouk Stein, Carol C Wu, et al.
Scientific Data|October 29, 2021
CLiP, catheter and line position datasetJennifer S N Tang, Jarrel C Y Seah, Adil Zia, et al.
Scientific Data|January 29, 2021
A patient-centric dataset of images and metadata for identifying melanomas using clinical contextVeronica Rotemberg, Nicholas Kurtansky, Brigid Betz-Stablein, et al.
Scientific Data|March 6, 2021
Author Correction: A patient-centric dataset of images and metadata for identifying melanomas using clinical contextVeronica Rotemberg, Nicholas Kurtansky, Brigid Betz-Stablein, et al.
Scientific Data|March 17, 2021
Publisher Correction: Author Correction: A patient-centric dataset of images and metadata for identifying melanomas using clinical contextVeronica Rotemberg, Nicholas Kurtansky, Brigid Betz-Stablein, et al.
Pageof 10