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Showing results (11-20 of 29) with videos related to

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Investigative Radiology|January 22, 2021
Automated Detection and Quantification of COVID-19 Airspace Disease on Chest Radiographs: A Novel Approach Achieving Expert Radiologist-Level Performance Using a Deep Convolutional Neural Network Trained on Digital Reconstructed Radiographs From Computed Tomography-Derived Ground TruthEduardo J Mortani Barbosa, Warren B Gefter, Florin C Ghesu, et al.
Scientific Reports|May 9, 2026
PatchCLIP enables region specific contrastive health record and image joint training with patch embedding lossSheethal Bhat, Awais Mansoor, Bogdan Georgescu, et al.
Journal of Nuclear Medicine : Official Publication, Society of Nuclear Medicine|April 26, 2020
Semiautomatically Quantified Tumor Volume Using <sup>68</sup>Ga-PSMA-11 PET as a Biomarker for Survival in Patients with Advanced Prostate CancerRobert Seifert, Ken Herrmann, Jens Kleesiek, et al.
Medicine|November 3, 2021
Detection and characterization of COVID-19 findings in chest CT: Feasibility and applicability of an AI-based software toolAndi Gashi, Rahel A Kubik-Huch, Vasiliki Chatzaraki, et al.
Scientific Reports|November 30, 2023
AUCReshaping: improved sensitivity at high-specificitySheethal Bhat, Awais Mansoor, Bogdan Georgescu, et al.
Journal of Applied Clinical Medical Physics|July 23, 2025
A multi-stage 3D convolutional neural network algorithm for CT-based lung segment parcellationTrishul Siddharthan, Zhoubing Xu, Bruce Spottiswoode, et al.
Journal of Thoracic Imaging|April 23, 2020
Machine Learning/Deep Neuronal Network: Routine Application in Chest Computed Tomography and Workflow ConsiderationsAndreas M Fischer, Basel Yacoub, Rock H Savage, et al.
Journal of Medical Imaging (Bellingham, Wash.)|December 5, 2022
Contrastive self-supervised learning from 100 million medical images with optional supervisionFlorin C Ghesu, Bogdan Georgescu, Awais Mansoor, et al.
Journal of Medical Imaging (Bellingham, Wash.)|June 20, 2022
Value of quantitative airspace disease measured on chest CT and chest radiography at initial diagnosis compared to clinical variables for prediction of severe COVID-19Hae-Min Jung, Rochelle Yang, Warren B Gefter, et al.
Radiology Advances|March 2, 2026
Deep learning-based pulmonary nodule risk assessment outperforms established malignancy risk scores in lung cancer screeningEduardo J Mortani Barbosa, Yohan Kim, Yanbo Zhang, et al.
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Showing results (11-20 of 29) with videos related to

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Investigative Radiology|January 22, 2021
Automated Detection and Quantification of COVID-19 Airspace Disease on Chest Radiographs: A Novel Approach Achieving Expert Radiologist-Level Performance Using a Deep Convolutional Neural Network Trained on Digital Reconstructed Radiographs From Computed Tomography-Derived Ground TruthEduardo J Mortani Barbosa, Warren B Gefter, Florin C Ghesu, et al.
Scientific Reports|May 9, 2026
PatchCLIP enables region specific contrastive health record and image joint training with patch embedding lossSheethal Bhat, Awais Mansoor, Bogdan Georgescu, et al.
Journal of Nuclear Medicine : Official Publication, Society of Nuclear Medicine|April 26, 2020
Semiautomatically Quantified Tumor Volume Using <sup>68</sup>Ga-PSMA-11 PET as a Biomarker for Survival in Patients with Advanced Prostate CancerRobert Seifert, Ken Herrmann, Jens Kleesiek, et al.
Medicine|November 3, 2021
Detection and characterization of COVID-19 findings in chest CT: Feasibility and applicability of an AI-based software toolAndi Gashi, Rahel A Kubik-Huch, Vasiliki Chatzaraki, et al.
Scientific Reports|November 30, 2023
AUCReshaping: improved sensitivity at high-specificitySheethal Bhat, Awais Mansoor, Bogdan Georgescu, et al.
Journal of Applied Clinical Medical Physics|July 23, 2025
A multi-stage 3D convolutional neural network algorithm for CT-based lung segment parcellationTrishul Siddharthan, Zhoubing Xu, Bruce Spottiswoode, et al.
Journal of Thoracic Imaging|April 23, 2020
Machine Learning/Deep Neuronal Network: Routine Application in Chest Computed Tomography and Workflow ConsiderationsAndreas M Fischer, Basel Yacoub, Rock H Savage, et al.
Journal of Medical Imaging (Bellingham, Wash.)|December 5, 2022
Contrastive self-supervised learning from 100 million medical images with optional supervisionFlorin C Ghesu, Bogdan Georgescu, Awais Mansoor, et al.
Journal of Medical Imaging (Bellingham, Wash.)|June 20, 2022
Value of quantitative airspace disease measured on chest CT and chest radiography at initial diagnosis compared to clinical variables for prediction of severe COVID-19Hae-Min Jung, Rochelle Yang, Warren B Gefter, et al.
Radiology Advances|March 2, 2026
Deep learning-based pulmonary nodule risk assessment outperforms established malignancy risk scores in lung cancer screeningEduardo J Mortani Barbosa, Yohan Kim, Yanbo Zhang, et al.
Pageof 3