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A B C D E F G H I J K L M N O P Q R S T U V W X Y Z # | show all

Keywords: Computer Vision
MO-E115-GePD-F1-2Chest X-Ray Quality Assurance Using Convolutional Networks: Identification of Clipped Images
R McBeth1*, D Huo2 , (1) University of Colorado Hospital, Aurora, CO, (2) University Colorado Denver, School of Medicine, Aurora, CO
SU-F-207-6Whole Skeleton Statistical Appearance Models Applied to the Detection of Abnormal Bone in NaF PET/CT Images
T Perk1*, M Scarpelli1 , T Bradshaw1 , S Chen2 , R Jeraj1,3 , (1) University of Wisconsin, Madison, WI (2) The 1st hospital of China Medical University, Shenyang, Liaoning (3) University of Ljubljana, Ljubljana, Slovenia
SU-H300-GePD-F6-3Development of An Automatic Deep Learning Framework for the Detection of Fiducial Markers in Intrafraction Kilovoltage Images
A Mylonas1*, P J Keall1, J T Booth2, T Eade2, D T Nguyen1, (1) ACRF Image X Institute, Sydney Medical School, University of Sydney, Camperdown, New South Wales, Australia, (2) Northern Sydney Cancer Centre, Royal North Shore Hospital, St Leonards, New South Wales, Australia
SU-H300-GePD-F8-3Image Based Lung Cancer Phenotyping with Deep-Learning Radiomics
T Chaunzwa1*, Y Xu2 , R Mak3 , D Christiani4 , M Lanuti5 , A Shafer6 , N Dia4 , H Aerts7 , (1) Howard Hughes Medical Institute, Chevy Chase, MD,(2) Harvard Medical School, Brigham and Women's Hosp., Boston, MA, (3) Brigham and Women's Hospital, Boston, MA, (4) Harvard T.H. Chan School of Public Health, Boston, MA, (5) Massachusetts General Hospital, Boston, MA, Boston, MA, (6) Boston, MA, (7) Dana-Farber/Brigham Women's Cancer Center, Boston, MA
SU-H300-GePD-F9-6Reproducibility of CT Iterative Reconstruction Algorithms From Analytic Reconstitutions with Convolutional Neural Networks for Pediatric Brain Imaging
R MacDougall*, Y Zhang , H Yu , UMass Lowell, Lowell, MA
SU-I-GPD-I-6Impact of Image Pre-Processing On Radiomics Feature Prediction Power in Recurrence Glioblastoma Patients
G Hajianfar1*, I Shiri2 ,M Oveisi3 , H Maleki4 , A Haghparast1 , (1) Kermanshah University of Medical Sciences,Kermanshah,Iran, (2)(3)(4) Rajaie Cardiovascular Medical and Research Center, Tehran,Iran
SU-I-GPD-J-67Densely Connected Semantic Segmentation Network for Liver Tumor Segmentation
J Kwon*, E Shim , Y Kim , K Choi , Korea Institute of Science and Technology (KIST), Seoul, Seoul
SU-I-GPD-T-51An Artificial Intelligent (AI) Tool for Estimating the Output Factor of Electron Cutouts
L Kofman1*, J Chang2 , (1) Tufts University, Medford, MA, (2) Northwell Health, Lake Success, NY
TH-EF-KDBRB1-6Abdominal Synthetic CT Generation for MR-Only Liver Radiotherapy Using Conditional Generative Adversarial Network
J Fu1*, A Santhanam1 , M Cao1 , M Guo1 , K Singhrao1 , V Yu1 , D Ruan1 , D Low1 , J Lewis1 , (1) UCLA School of Medicine, Los Angeles, CA
TH-EF-KDBRB1-7Deep Learning Approaches for Male Pelvic Synthetic CT Generation Using 2D and 3D Convolutional Neural Networks
J Fu*, Y Yang , K Singhrao , D Ruan , A Kishan , C King , D Low , J Lewis , UCLA, Los Angeles, CA
TU-C1000-GePD-F8-1Comparison of Automated PET Segmentation Methods in Lymphoma
A Weisman1*, T Bradshaw1, Minnie Kieler1 R Jeraj1,2 (1) University of Wisconsin-Madison, Madison, WI, (2) University of Ljubljana, Ljubljana, Slovenia
TU-C1030-GePD-F1-3Comparison of Noise Analysis and Deep Learning-Based Image Quality Assessment (IQA) Methods for Thoracic Computed Tomography (CT)
B Grant1*, J Lee2 , J Chung3 , I Reiser2 , L Lan2 , J Papaioannou3 , M Giger2 , (1) Western Kentucky University, Bowling Green, KY, (2) The University of Chicago, Chicago, IL, (3) University of Chicago Medicine, Chicago, IL,
TU-C930-GePD-F5-1Automatic Segmentation of Vertebrae Using Deep Learning and Generative Adversarial Networks
E Boehnke*, A Santhanam , K Sheng , UCLA School of Medicine, Los Angeles, CA
TU-K-202-7Machine Learning On Quality Control of Chest CT Chest Exams: Scan Length Optimization
D Huo*, A Scherzinger , University Colorado Denver, School of Medicine, Aurora, CO
WE-C1030-GePD-F5-3Generative Adversarial Network for Undersampled Brain MRI Reconstruction
N Zhao*, K Sheng , UCLA School of Medicine, Los Angeles, CA
WE-FG-207-3Deep Learning for Automated Quantification of Tumor Phenotypes
A Hosny1*, T Coroller1 , P Grossmann1 , C Parmar1 , R Zeleznik1 , A Kumar1 , J Bussink2 , R Gillies3 , R Mak4 , H Aerts1 , (1) Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women Hospital, Harvard Medical School, Boston, MA, (2) Radboud University, Nijmegen, Nijmegen, (3) Moffitt Cancer Center, Tampa, FL, (4) Brigham and Women's Hospital, Boston, MA