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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 Study
Hiram 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 Treatment
Haley 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 Classification
Simukayi 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 database
Michael 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 quantification
Michael 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 Radiographs
Michael 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 Data
Richard 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 Dataset
Richard 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 Enhancement
Richard 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 Radiographs
Ian Pan, Grayson L Baird, Simukayi Mutasa, et al.
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Search research articles
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Showing results (11-20 of 37) with videos related to
Sort By:
Page
of 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 Study
Hiram 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 Treatment
Haley 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 Classification
Simukayi 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 database
Michael 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 quantification
Michael 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 Radiographs
Michael 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 Data
Richard 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 Dataset
Richard 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 Enhancement
Richard 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 Radiographs
Ian Pan, Grayson L Baird, Simukayi Mutasa, et al.
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of 4