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Ziba Gandomkar

Showing results (11-20 of 48) with videos related to

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The British Journal of Radiology|January 24, 2024
Computer-extracted global radiomic features can predict the radiologists' first impression about the abnormality of a screening mammogramSomphone Siviengphanom, Sarah J Lewis, Patrick C Brennan, et al.
Medical Physics|April 26, 2018
Recurrence quantification analysis of radiologists' scanpaths when interpreting mammogramsZiba Gandomkar, Kevin Tay, Patrick C Brennan, et al.
Academic Radiology|November 20, 2021
Mammography-based Radiomics in Breast Cancer: A Scoping Review of Current Knowledge and Future NeedsSomphone Siviengphanom, Ziba Gandomkar, Sarah J Lewis, et al.
Journal of Digital Imaging|May 30, 2023
Global Radiomic Features from Mammography for Predicting Difficult-To-Interpret Normal CasesSomphone Siviengphanom, Ziba Gandomkar, Sarah J Lewis, et al.
Journal of Personalized Medicine|June 28, 2023
Using Radiomics-Based Machine Learning to Create Targeted Test Sets to Improve Specific Mammography Reader Cohort Performance: A Feasibility StudyXuetong Tao, Ziba Gandomkar, Tong Li, et al.
Medical Physics|September 1, 2018
Can eye-tracking metrics be used to better pair radiologists in a mammogram reading task?Ziba Gandomkar, Kevin Tay, Patrick C Brennan, et al.
Asia-Pacific Journal of Clinical Oncology|April 7, 2023
Incidence, mortality, survival, and disease burden of breast cancer in China compared to other developed countriesXuetong Tao, Tong Li, Ziba Gandomkar, et al.
IEEE Transactions on Medical Imaging|January 6, 2017
iCAP: An Individualized Model Combining Gaze Parameters and Image-Based Features to Predict Radiologists' Decisions While Reading MammogramsZiba Gandomkar, Kevin Tay, Will Ryder, et al.
Journal of Medical Imaging (Bellingham, Wash.)|October 7, 2024
Optimizing mammography interpretation education: leveraging deep learning for cohort-specific error detection to enhance radiologist trainingXuetong Tao, Warren M Reed, Tong Li, et al.
The British Journal of Radiology|October 9, 2024
Radiomic analysis of cohort-specific diagnostic errors in reading dense mammograms using artificial intelligenceXuetong Tao, Ziba Gandomkar, Tong Li, et al.
Pageof 5

Showing results (11-20 of 48) with videos related to

Sort By:
Pageof 5
The British Journal of Radiology|January 24, 2024
Computer-extracted global radiomic features can predict the radiologists' first impression about the abnormality of a screening mammogramSomphone Siviengphanom, Sarah J Lewis, Patrick C Brennan, et al.
Medical Physics|April 26, 2018
Recurrence quantification analysis of radiologists' scanpaths when interpreting mammogramsZiba Gandomkar, Kevin Tay, Patrick C Brennan, et al.
Academic Radiology|November 20, 2021
Mammography-based Radiomics in Breast Cancer: A Scoping Review of Current Knowledge and Future NeedsSomphone Siviengphanom, Ziba Gandomkar, Sarah J Lewis, et al.
Journal of Digital Imaging|May 30, 2023
Global Radiomic Features from Mammography for Predicting Difficult-To-Interpret Normal CasesSomphone Siviengphanom, Ziba Gandomkar, Sarah J Lewis, et al.
Journal of Personalized Medicine|June 28, 2023
Using Radiomics-Based Machine Learning to Create Targeted Test Sets to Improve Specific Mammography Reader Cohort Performance: A Feasibility StudyXuetong Tao, Ziba Gandomkar, Tong Li, et al.
Medical Physics|September 1, 2018
Can eye-tracking metrics be used to better pair radiologists in a mammogram reading task?Ziba Gandomkar, Kevin Tay, Patrick C Brennan, et al.
Asia-Pacific Journal of Clinical Oncology|April 7, 2023
Incidence, mortality, survival, and disease burden of breast cancer in China compared to other developed countriesXuetong Tao, Tong Li, Ziba Gandomkar, et al.
IEEE Transactions on Medical Imaging|January 6, 2017
iCAP: An Individualized Model Combining Gaze Parameters and Image-Based Features to Predict Radiologists' Decisions While Reading MammogramsZiba Gandomkar, Kevin Tay, Will Ryder, et al.
Journal of Medical Imaging (Bellingham, Wash.)|October 7, 2024
Optimizing mammography interpretation education: leveraging deep learning for cohort-specific error detection to enhance radiologist trainingXuetong Tao, Warren M Reed, Tong Li, et al.
The British Journal of Radiology|October 9, 2024
Radiomic analysis of cohort-specific diagnostic errors in reading dense mammograms using artificial intelligenceXuetong Tao, Ziba Gandomkar, Tong Li, et al.
Pageof 5