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Heber MacMahon

Showing results (21-30 of 102) with videos related to

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Journal of Medical Screening|August 14, 2014
Geographic variation in radiologist capacity and widespread implementation of lung cancer CT screeningFabrice Smieliauskas, Heber MacMahon, Ravi Salgia, et al.
Journal of Thoracic Imaging|June 4, 2008
Dual energy subtraction and temporal subtraction chest radiographyHeber MacMahon, Feng Li, Roger Engelmann, et al.
Medical Physics|May 30, 2002
Automated computerized scheme for distinction between benign and malignant solitary pulmonary nodules on chest imagesMasahito Aoyama, Qiang Li, Shigehiko Katsuragawa, et al.
Academic Radiology|January 18, 2016
ROC Curve for Extremely Subtle Lung Nodules on Chest Radiographs Confirmed by CT ScanHua Shang, Yulei Jiang, Feng Li, et al.
Breast Cancer Research and Treatment|August 10, 2016
Clinical significance of noncalcified lung nodules in patients with breast cancerFeng Li, Samuel G Armato, Maryellen L Giger, et al.
Academic Radiology|February 22, 2005
False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural networkKenji Suzuki, Junji Shiraishi, Hiroyuki Abe, et al.
Radiology|October 23, 2004
Malignant versus benign nodules at CT screening for lung cancer: comparison of thin-section CT findingsFeng Li, Shusuke Sone, Hiroyuki Abe, et al.
Chest|January 10, 2021
Criteria for Low-Dose CT Lung Cancer Screening in the Setting of Air Pollution: A Discussion That Is Long OverdueVineet K Khanna, Subha Ghosh, Heber MacMahon, et al.
Academic Radiology|March 29, 2002
Use of an artificial neural network to determine the diagnostic value of specific clinical and radiologic parameters in the diagnosis of interstitial lung disease on chest radiographsHiroyuki Abe, Kazuto Ashizawa, Shigehiko Katsuragawa, et al.
Journal of Medical Imaging (Bellingham, Wash.)|February 12, 2020
Deep convolutional neural networks in the classification of dual-energy thoracic radiographic views for efficient workflow: analysis on over 6500 clinical radiographsJennie Crosby, Thomas Rhines, Feng Li, et al.
Pageof 11

Showing results (21-30 of 102) with videos related to

Sort By:
Pageof 11
Journal of Medical Screening|August 14, 2014
Geographic variation in radiologist capacity and widespread implementation of lung cancer CT screeningFabrice Smieliauskas, Heber MacMahon, Ravi Salgia, et al.
Journal of Thoracic Imaging|June 4, 2008
Dual energy subtraction and temporal subtraction chest radiographyHeber MacMahon, Feng Li, Roger Engelmann, et al.
Medical Physics|May 30, 2002
Automated computerized scheme for distinction between benign and malignant solitary pulmonary nodules on chest imagesMasahito Aoyama, Qiang Li, Shigehiko Katsuragawa, et al.
Academic Radiology|January 18, 2016
ROC Curve for Extremely Subtle Lung Nodules on Chest Radiographs Confirmed by CT ScanHua Shang, Yulei Jiang, Feng Li, et al.
Breast Cancer Research and Treatment|August 10, 2016
Clinical significance of noncalcified lung nodules in patients with breast cancerFeng Li, Samuel G Armato, Maryellen L Giger, et al.
Academic Radiology|February 22, 2005
False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural networkKenji Suzuki, Junji Shiraishi, Hiroyuki Abe, et al.
Radiology|October 23, 2004
Malignant versus benign nodules at CT screening for lung cancer: comparison of thin-section CT findingsFeng Li, Shusuke Sone, Hiroyuki Abe, et al.
Chest|January 10, 2021
Criteria for Low-Dose CT Lung Cancer Screening in the Setting of Air Pollution: A Discussion That Is Long OverdueVineet K Khanna, Subha Ghosh, Heber MacMahon, et al.
Academic Radiology|March 29, 2002
Use of an artificial neural network to determine the diagnostic value of specific clinical and radiologic parameters in the diagnosis of interstitial lung disease on chest radiographsHiroyuki Abe, Kazuto Ashizawa, Shigehiko Katsuragawa, et al.
Journal of Medical Imaging (Bellingham, Wash.)|February 12, 2020
Deep convolutional neural networks in the classification of dual-energy thoracic radiographic views for efficient workflow: analysis on over 6500 clinical radiographsJennie Crosby, Thomas Rhines, Feng Li, et al.
Pageof 11