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Kyle A Hasenstab

Showing results (1-10 of 15) with videos related to

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Radiology. Cardiothoracic Imaging|December 12, 2024
Evaluating the Cumulative Benefit of Inspiratory CT, Expiratory CT, and Clinical Data for COPD Diagnosis and Staging through Deep LearningAmanda N Lee, Albert Hsiao, Kyle A Hasenstab
Scientific Reports|October 18, 2024
Simulating clinical features on chest radiographs for medical image exploration and CNN explainability using a style-based generative adversarial autoencoderKyle A Hasenstab, Lewis Hahn, Nick Chao, et al.
Radiology. Artificial Intelligence|February 11, 2022
CNN-based Deformable Registration Facilitates Fast and Accurate Air Trapping Measurements at Inspiratory and Expiratory CTKyle A Hasenstab, Joseph Tabalon, Nancy Yuan, et al.
Radiology. Artificial Intelligence|February 14, 2022
Erratum: CNN-based Deformable Registration Facilitates Fast and Accurate Air Trapping Measurements at Inspiratory and Expiratory CTKyle A Hasenstab, Joseph Tabalon, Nancy Yuan, et al.
Journal of Imaging Informatics in Medicine|February 6, 2024
Signal Intensity Trajectories Clustering for Liver Vasculature Segmentation and Labeling (LiVaS) on Contrast-Enhanced MR Images: A Feasibility Pilot StudyMladen Zecevic, Kyle A Hasenstab, Kang Wang, et al.
Radiology. Cardiothoracic Imaging|July 5, 2022
Erratum: Automated CT Staging of Chronic Obstructive Pulmonary Disease Severity for Predicting Disease Progression and Mortality with a Deep Learning Convolutional Neural NetworkKyle A Hasenstab, Nancy Yuan, Tara Retson, et al.
Radiology. Cardiothoracic Imaging|May 10, 2021
Automated CT Staging of Chronic Obstructive Pulmonary Disease Severity for Predicting Disease Progression and Mortality with a Deep Learning Convolutional Neural NetworkKyle A Hasenstab, Nancy Yuan, Tara Retson, et al.
Abdominal Radiology (New York)|May 31, 2025
Relationship between spleen volume and diameter for assessment of response to treatment on CT in patients with hematologic malignancies enrolled in clinical trialsKyle A Hasenstab, Jie Lu, Lambert T Leong, et al.
Radiology. Artificial Intelligence|April 8, 2022
Reader Perceptions and Impact of AI on CT Assessment of Air TrappingTara A Retson, Kyle A Hasenstab, Seth J Kligerman, et al.
European Journal of Radiology|January 21, 2020
Convolutional neural network-automated hepatobiliary phase adequacy evaluation may optimize examination timeGuilherme Moura Cunha, Kyle A Hasenstab, Atsushi Higaki, et al.
Pageof 2

Showing results (1-10 of 15) with videos related to

Sort By:
Pageof 2
Radiology. Cardiothoracic Imaging|December 12, 2024
Evaluating the Cumulative Benefit of Inspiratory CT, Expiratory CT, and Clinical Data for COPD Diagnosis and Staging through Deep LearningAmanda N Lee, Albert Hsiao, Kyle A Hasenstab
Scientific Reports|October 18, 2024
Simulating clinical features on chest radiographs for medical image exploration and CNN explainability using a style-based generative adversarial autoencoderKyle A Hasenstab, Lewis Hahn, Nick Chao, et al.
Radiology. Artificial Intelligence|February 11, 2022
CNN-based Deformable Registration Facilitates Fast and Accurate Air Trapping Measurements at Inspiratory and Expiratory CTKyle A Hasenstab, Joseph Tabalon, Nancy Yuan, et al.
Radiology. Artificial Intelligence|February 14, 2022
Erratum: CNN-based Deformable Registration Facilitates Fast and Accurate Air Trapping Measurements at Inspiratory and Expiratory CTKyle A Hasenstab, Joseph Tabalon, Nancy Yuan, et al.
Journal of Imaging Informatics in Medicine|February 6, 2024
Signal Intensity Trajectories Clustering for Liver Vasculature Segmentation and Labeling (LiVaS) on Contrast-Enhanced MR Images: A Feasibility Pilot StudyMladen Zecevic, Kyle A Hasenstab, Kang Wang, et al.
Radiology. Cardiothoracic Imaging|July 5, 2022
Erratum: Automated CT Staging of Chronic Obstructive Pulmonary Disease Severity for Predicting Disease Progression and Mortality with a Deep Learning Convolutional Neural NetworkKyle A Hasenstab, Nancy Yuan, Tara Retson, et al.
Radiology. Cardiothoracic Imaging|May 10, 2021
Automated CT Staging of Chronic Obstructive Pulmonary Disease Severity for Predicting Disease Progression and Mortality with a Deep Learning Convolutional Neural NetworkKyle A Hasenstab, Nancy Yuan, Tara Retson, et al.
Abdominal Radiology (New York)|May 31, 2025
Relationship between spleen volume and diameter for assessment of response to treatment on CT in patients with hematologic malignancies enrolled in clinical trialsKyle A Hasenstab, Jie Lu, Lambert T Leong, et al.
Radiology. Artificial Intelligence|April 8, 2022
Reader Perceptions and Impact of AI on CT Assessment of Air TrappingTara A Retson, Kyle A Hasenstab, Seth J Kligerman, et al.
European Journal of Radiology|January 21, 2020
Convolutional neural network-automated hepatobiliary phase adequacy evaluation may optimize examination timeGuilherme Moura Cunha, Kyle A Hasenstab, Atsushi Higaki, et al.
Pageof 2