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MinJae Woo

Showing results (1-10 of 11) with videos related to

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Medrxiv : the Preprint Server for Health Sciences|July 16, 2025
Enhancing Pandemic Prediction: A Deep Learning Approach Using Transformer Neural Networks and Multi-Source Data Fusion for Infectious Disease ForecastingJiande Wu, Shakhawat Tanim, MinJae Woo, et al.
Current Problems in Diagnostic Radiology|February 5, 2020
Intervention to Reduce Interobserver Variability in Computed Tomographic Measurement of Cancer Lesions Among Experienced RadiologistsMinJae Woo, Steven C Lowe, A Michael Devane, et al.
BMJ Open|November 16, 2020
Retrospective comparison of approaches to evaluating inter-observer variability in CT tumour measurements in an academic health centreMinJae Woo, Moonseong Heo, A Michael Devane, et al.
Epidemics|November 6, 2025
A deep learning approach for enhancing pandemic prediction: A retrospective evaluation of transformer neural networks and multi-source data fusion for infectious disease forecastingJiande Wu, Shakhawat Tanim, MinJae Woo, et al.
Cancer Imaging : the Official Publication of the International Cancer Imaging Society|June 24, 2021
Deep learning for semi-automated unidirectional measurement of lung tumor size in CTMinJae Woo, A Michael Devane, Steven C Lowe, et al.
Current Problems in Diagnostic Radiology|February 1, 2024
Deep learning for computer-aided abnormalities classification in digital mammogram: A data-centric perspectiveVineela Nalla, Seyedamin Pouriyeh, Reza M Parizi, et al.
PLOS Digital Health|April 8, 2025
Subgroup evaluation to understand performance gaps in deep learning-based classification of regions of interest on mammographyMinJae Woo, Linglin Zhang, Beatrice Brown-Mulry, et al.
Frontiers in Radiology|August 17, 2023
Impact of multi-source data augmentation on performance of convolutional neural networks for abnormality classification in mammographyInChan Hwang, Hari Trivedi, Beatrice Brown-Mulry, et al.
Nature Communications|March 20, 2026
Subgroup performance of a commercial digital breast tomosynthesis model for breast cancer detectionBeatrice Brown-Mulry, Rohan Satya Isaac, Sang Hyup Lee, et al.
Radiology. Artificial Intelligence|February 1, 2023
The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.4 Million Screening and Diagnostic Mammographic ImagesJiwoong J Jeong, Brianna L Vey, Ananth Bhimireddy, et al.
Pageof 2

Showing results (1-10 of 11) with videos related to

Sort By:
Pageof 2
Medrxiv : the Preprint Server for Health Sciences|July 16, 2025
Enhancing Pandemic Prediction: A Deep Learning Approach Using Transformer Neural Networks and Multi-Source Data Fusion for Infectious Disease ForecastingJiande Wu, Shakhawat Tanim, MinJae Woo, et al.
Current Problems in Diagnostic Radiology|February 5, 2020
Intervention to Reduce Interobserver Variability in Computed Tomographic Measurement of Cancer Lesions Among Experienced RadiologistsMinJae Woo, Steven C Lowe, A Michael Devane, et al.
BMJ Open|November 16, 2020
Retrospective comparison of approaches to evaluating inter-observer variability in CT tumour measurements in an academic health centreMinJae Woo, Moonseong Heo, A Michael Devane, et al.
Epidemics|November 6, 2025
A deep learning approach for enhancing pandemic prediction: A retrospective evaluation of transformer neural networks and multi-source data fusion for infectious disease forecastingJiande Wu, Shakhawat Tanim, MinJae Woo, et al.
Cancer Imaging : the Official Publication of the International Cancer Imaging Society|June 24, 2021
Deep learning for semi-automated unidirectional measurement of lung tumor size in CTMinJae Woo, A Michael Devane, Steven C Lowe, et al.
Current Problems in Diagnostic Radiology|February 1, 2024
Deep learning for computer-aided abnormalities classification in digital mammogram: A data-centric perspectiveVineela Nalla, Seyedamin Pouriyeh, Reza M Parizi, et al.
PLOS Digital Health|April 8, 2025
Subgroup evaluation to understand performance gaps in deep learning-based classification of regions of interest on mammographyMinJae Woo, Linglin Zhang, Beatrice Brown-Mulry, et al.
Frontiers in Radiology|August 17, 2023
Impact of multi-source data augmentation on performance of convolutional neural networks for abnormality classification in mammographyInChan Hwang, Hari Trivedi, Beatrice Brown-Mulry, et al.
Nature Communications|March 20, 2026
Subgroup performance of a commercial digital breast tomosynthesis model for breast cancer detectionBeatrice Brown-Mulry, Rohan Satya Isaac, Sang Hyup Lee, et al.
Radiology. Artificial Intelligence|February 1, 2023
The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.4 Million Screening and Diagnostic Mammographic ImagesJiwoong J Jeong, Brianna L Vey, Ananth Bhimireddy, et al.
Pageof 2