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Joseph Nathanael Witanto

Showing results (1-10 of 7) with videos related to

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Abdominal Radiology (New York)|May 24, 2023
Development of a deep-learning model for classification of LI-RADS major features by using subtraction images of MRI: a preliminary studyJunghoan Park, Jae Seok Bae, Jong-Min Kim, et al.
European Radiology|May 19, 2021
Automatic pulmonary vessel segmentation on noncontrast chest CT: deep learning algorithm developed using spatiotemporally matched virtual noncontrast images and low-keV contrast-enhanced vessel mapsJu Gang Nam, Joseph Nathanael Witanto, Sang Joon Park, et al.
Quantitative Imaging in Medicine and Surgery|February 23, 2023
Mycobacterial cavity on chest computed tomography: clinical implications and deep learning-based automatic detection with quantificationIeun Yoon, Jung Hee Hong, Joseph Nathanael Witanto, et al.
Radiology|October 25, 2022
Deep Learning for Estimating Lung Capacity on Chest Radiographs Predicts Survival in Idiopathic Pulmonary FibrosisHyungjin Kim, Kwang Nam Jin, Seung-Jin Yoo, et al.
European Journal of Radiology|May 20, 2023
Generative adversarial network for automatic quantification of Coronavirus disease 2019 pneumonia on chest radiographsSeung-Jin Yoo, Hyungjin Kim, Joseph Nathanael Witanto, et al.
Clinical Nutrition (Edinburgh, Scotland)|August 8, 2021
Deep neural network for automatic volumetric segmentation of whole-body CT images for body composition assessmentYoon Seong Lee, Namki Hong, Joseph Nathanael Witanto, et al.
Journal of Magnetic Resonance Imaging : JMRI|July 1, 2022
Fully Automated MRI Segmentation and Volumetric Measurement of Intracranial Meningioma Using Deep LearningHo Kang, Joseph Nathanael Witanto, Kevin Pratama, et al.
Pageof 1

Showing results (1-10 of 7) with videos related to

Sort By:
Pageof 1
Abdominal Radiology (New York)|May 24, 2023
Development of a deep-learning model for classification of LI-RADS major features by using subtraction images of MRI: a preliminary studyJunghoan Park, Jae Seok Bae, Jong-Min Kim, et al.
European Radiology|May 19, 2021
Automatic pulmonary vessel segmentation on noncontrast chest CT: deep learning algorithm developed using spatiotemporally matched virtual noncontrast images and low-keV contrast-enhanced vessel mapsJu Gang Nam, Joseph Nathanael Witanto, Sang Joon Park, et al.
Quantitative Imaging in Medicine and Surgery|February 23, 2023
Mycobacterial cavity on chest computed tomography: clinical implications and deep learning-based automatic detection with quantificationIeun Yoon, Jung Hee Hong, Joseph Nathanael Witanto, et al.
Radiology|October 25, 2022
Deep Learning for Estimating Lung Capacity on Chest Radiographs Predicts Survival in Idiopathic Pulmonary FibrosisHyungjin Kim, Kwang Nam Jin, Seung-Jin Yoo, et al.
European Journal of Radiology|May 20, 2023
Generative adversarial network for automatic quantification of Coronavirus disease 2019 pneumonia on chest radiographsSeung-Jin Yoo, Hyungjin Kim, Joseph Nathanael Witanto, et al.
Clinical Nutrition (Edinburgh, Scotland)|August 8, 2021
Deep neural network for automatic volumetric segmentation of whole-body CT images for body composition assessmentYoon Seong Lee, Namki Hong, Joseph Nathanael Witanto, et al.
Journal of Magnetic Resonance Imaging : JMRI|July 1, 2022
Fully Automated MRI Segmentation and Volumetric Measurement of Intracranial Meningioma Using Deep LearningHo Kang, Joseph Nathanael Witanto, Kevin Pratama, et al.
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