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Simukayi Mutasa

Showing results (11-20 of 37) with videos related to

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AJR. American Journal of Roentgenology|December 13, 2018
Prediction of Lymph Node Maximum Standardized Uptake Value in Patients With Cancer Using a 3D Convolutional Neural Network: A Proof-of-Concept StudyHiram Shaish, Simukayi Mutasa, Jasnit Makkar, et al.
Clinical Breast Cancer|December 5, 2020
Dynamic Changes of Convolutional Neural Network-based Mammographic Breast Cancer Risk Score Among Women Undergoing Chemoprevention TreatmentHaley Manley, Simukayi Mutasa, Peter Chang, et al.
Journal of Digital Imaging|June 26, 2020
Advanced Deep Learning Techniques Applied to Automated Femoral Neck Fracture Detection and ClassificationSimukayi Mutasa, Sowmya Varada, Akshay Goel, et al.
Magnetic Resonance Imaging|September 5, 2020
A novel CNN algorithm for pathological complete response prediction using an I-SPY TRIAL breast MRI databaseMichael Z Liu, Simukayi Mutasa, Peter Chang, et al.
Computers in Biology and Medicine|July 14, 2020
Channel width optimized neural networks for liver and vessel segmentation in liver iron quantificationMichael Liu, Rami Vanguri, Simukayi Mutasa, et al.
Journal of Imaging Informatics in Medicine|February 12, 2024
Deep Learning-Assisted Identification of Femoroacetabular Impingement (FAI) on Routine Pelvic RadiographsMichael K Hoy, Vishal Desai, Simukayi Mutasa, et al.
AJR. American Journal of Roentgenology|March 13, 2019
Accuracy of Distinguishing Atypical Ductal Hyperplasia From Ductal Carcinoma In Situ With Convolutional Neural Network-Based Machine Learning Approach Using Mammographic Image DataRichard Ha, Simukayi Mutasa, Eduardo Pascual Van Sant, et al.
Academic Radiology|August 4, 2018
Convolutional Neural Network Based Breast Cancer Risk Stratification Using a Mammographic DatasetRichard Ha, Peter Chang, Jenika Karcich, et al.
Journal of Digital Imaging|August 5, 2018
Fully Automated Convolutional Neural Network Method for Quantification of Breast MRI Fibroglandular Tissue and Background Parenchymal EnhancementRichard Ha, Peter Chang, Eralda Mema, et al.
Radiology. Artificial Intelligence|May 3, 2021
Rethinking Greulich and Pyle: A Deep Learning Approach to Pediatric Bone Age Assessment Using Pediatric Trauma Hand RadiographsIan Pan, Grayson L Baird, Simukayi Mutasa, et al.
Pageof 4

Showing results (11-20 of 37) with videos related to

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Pageof 4
AJR. American Journal of Roentgenology|December 13, 2018
Prediction of Lymph Node Maximum Standardized Uptake Value in Patients With Cancer Using a 3D Convolutional Neural Network: A Proof-of-Concept StudyHiram Shaish, Simukayi Mutasa, Jasnit Makkar, et al.
Clinical Breast Cancer|December 5, 2020
Dynamic Changes of Convolutional Neural Network-based Mammographic Breast Cancer Risk Score Among Women Undergoing Chemoprevention TreatmentHaley Manley, Simukayi Mutasa, Peter Chang, et al.
Journal of Digital Imaging|June 26, 2020
Advanced Deep Learning Techniques Applied to Automated Femoral Neck Fracture Detection and ClassificationSimukayi Mutasa, Sowmya Varada, Akshay Goel, et al.
Magnetic Resonance Imaging|September 5, 2020
A novel CNN algorithm for pathological complete response prediction using an I-SPY TRIAL breast MRI databaseMichael Z Liu, Simukayi Mutasa, Peter Chang, et al.
Computers in Biology and Medicine|July 14, 2020
Channel width optimized neural networks for liver and vessel segmentation in liver iron quantificationMichael Liu, Rami Vanguri, Simukayi Mutasa, et al.
Journal of Imaging Informatics in Medicine|February 12, 2024
Deep Learning-Assisted Identification of Femoroacetabular Impingement (FAI) on Routine Pelvic RadiographsMichael K Hoy, Vishal Desai, Simukayi Mutasa, et al.
AJR. American Journal of Roentgenology|March 13, 2019
Accuracy of Distinguishing Atypical Ductal Hyperplasia From Ductal Carcinoma In Situ With Convolutional Neural Network-Based Machine Learning Approach Using Mammographic Image DataRichard Ha, Simukayi Mutasa, Eduardo Pascual Van Sant, et al.
Academic Radiology|August 4, 2018
Convolutional Neural Network Based Breast Cancer Risk Stratification Using a Mammographic DatasetRichard Ha, Peter Chang, Jenika Karcich, et al.
Journal of Digital Imaging|August 5, 2018
Fully Automated Convolutional Neural Network Method for Quantification of Breast MRI Fibroglandular Tissue and Background Parenchymal EnhancementRichard Ha, Peter Chang, Eralda Mema, et al.
Radiology. Artificial Intelligence|May 3, 2021
Rethinking Greulich and Pyle: A Deep Learning Approach to Pediatric Bone Age Assessment Using Pediatric Trauma Hand RadiographsIan Pan, Grayson L Baird, Simukayi Mutasa, et al.
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