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Keywords: computer vision
MO-A-BRC-4Prospective Clinical Experiences of Using the First Pediatric Deep-Learning Auto-Contouring models for Cranio-Spinal Irradiation (CSI)
S Zieminski*, S M MacDonald, T I Yock, Y Wang, Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
MO-H345-IePD-F2-1Integration of Attention-GAN to Predict Mid-Treatment Dual-Energy CT for Head and Neck Adaptive Radiation Therapy
Y Yan1,2*, R BAYLISS1, H Emami Gohari3, M Dong3, C Glide-Hurst1,2, (1) Department of Human Oncology, University of Wisconsin-Madison, Madison, WI, (2) Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, (3) Department of Computer Science, Wayne State University, Detroit, MI
PO-GePV-E-8Strategies to Increase Data Science and Artificial Intelligence Curriculum and Practical Skills in Medical Physics Graduate and Residency Programs
R McBeth*, T Li, S O'Reilly, University of Pennsylvania, Philadelphia, PA
PO-GePV-I-61Personalized Patient-Adaptive Sparse-View CT Deep Reconstruction
B Song*, L Shen, L Xing, Stanford University School of Medicine, Stanford, CA
PO-GePV-M-35Convolutional Neural Networks for the Automated Segmentation of Malignant Pleural Mesothelioma: Analysis of Performance Based On Probability Map Threshold
M Shenouda1*, E Gudmundsson2, F Li1, C Straus1, H Kindler1, A Dudek3, T Stinchcombe4, X Wang4, A Starkey1, S Armato1, (1) The University of Chicago, Chicago, IL, (2) UCL Hospitals NHS Trust, UK, (3) Health Partners Institute, HealthPartners Cancer Care Center, St. Paul, MN, (4) Duke University, Durham, NC
PO-GePV-M-60Assessing Role of Feature Selection in Deep Transfer Classification of Indeterminant Thyroid Nodules
JL Cozzi*, H Li, J Williams, J Conn Busch, L Lan, XM Keutgen, ML Giger, University of Chicago, Chicago, IL
PO-GePV-M-163Daily Coverage Assessment Using Deep Learning Generated Synthetic CT for Lung SBRT Patients
R LJ Qiu1, Y Lei1, J Shelton1,2, K Higgins1, J Bradley1, W Curran1, T Liu1, A Kesarwala1, X Yang1, (1) Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA (2) Department of Radiation Oncology, Grady Memorial Hospital, Atlanta, GA
PO-GePV-M-287A Two-Step Method to Improve Image Quality of CBCT with Phantom-Based Supervised and Patient-Based Unsupervised Learning Strategies
Y Liu1,2*, X Chen1, J Zhu1, B Yang1, R Wei1, R Xiong2, H Quan2, Y Liu1, J Dai1, K Men1, (1) National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Beijing, 100021 ,CN, (2) Wuhan University, Wuhan, 430072 ,CN,
PO-GePV-M-320TransCBCT: Improving the Image Quality of Cone-Beam Computed Tomography with Transformer
X Chen1*, Y Liu1, B Yang1, J Zhu1, Y Liu1, J Dai1, K Men1, (1) National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Beijing, 100021,CN
SU-E-BRB-8Task-Specific Fine-Tuning for Interactive Deep Learning Segmentation for Lung Fibrosis On CT Post Radiotherapy
MJ Trimpl1,2,3*, P Salome4,5,6,7, D Walz4,5,6,7, J Hoerner-rieber6,7, S Regnery6,7, EPJ Stride1, KA Vallis3, J Debus6,7, A Abdollahi4,5,6,7, MJ Gooding2, M Knoll4,5,6,7, (1) Institute of Biomedical Engineering, Department of Engineering Science, Oxford, GB, (2) Mirada Medical, Oxford, GB, (3) Oxford Institute for Radiation Oncology, Oxford, GB, (4) CCU Translational Radiation Oncology, German Cancer Consortium (DKTK) Core-Center Heidelberg, Heidelberg University Hospital (UKHD) and German Cancer Research Center (DKFZ), Heidelberg, DE, (5)Division of Molecular and Translational Radiation Oncology, Heidelberg Faculty of Medicine (MFHD), Heidelberg University Hospital (UKHD), Heidelberg Ion-Beam Therapy Center (HIT), Heidelberg, DE, (6) Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, DE, (7) National Center for Radiation Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, DE
SU-F-201-1A Weakly-Supervised Approach for Automatic Selection of Uniform Regions in Clinical CT Images
W Cao*, P Korfiatis, T Kline, M Callstrom, A Missert, Mayo Clinic, Rochester, MN
SU-F-201-2Improving Cone-Beam CT Auto-Segmentation Accuracy Using Cycle GANs for Domain Adaptation
K Shah1*, J Shackleford1, N Kandasamy1, G Sharp2, (1) Drexel University, Philadelphia, PA, (2)Massachusetts General Hospital, Boston, MA
SU-H330-IePD-F5-2Deep Learning Projection Interpolation for Sparsely Sampled Real Patient CBCT Reconstruction
K Lu1*, Z Zhang1, L Ren2, F Yin1,3, (1) Duke University, Durham, NC, (2) University of Maryland, Baltimore, MD, (3) Duke University Medical Center, Durham, NC
SU-H330-IePD-F9-4A Combined Radiomic and Clinical Model for the Differential Diagnosis of Pneumonitis in Patients with NSCLC (Non-Small Cell Lung Cancer) Patients Receiving Immunotherapy (IO Therapy)
A Traverso1*, F Tohidinezhad1, D Bontempi1, A Dekker1, L Hendriks2, D De Ruysscher1, (1) MAASTRO Clinic, Maastricht, NL (2) Maastricht University Medical Centre, Maastricht, NL
SU-H430-IePD-F6-1Deep Fusion of CT Imaging and Blood Markers to Estimate Portal Venous Hypertension: Novel Optimization of Data Fusion Quality
Y Wang*, X Li, M Konanur, B Konkel, E Seyferth, N Brajer, J Liu, M Bashir, K Lafata, Duke University, Durham, NC
SU-H430-IePD-F6-3Curation of a Large Public Head and Neck Dataset for Machine Learning (RADCURE) Using An Automated Data Mining Platform
M Welch1, T Patel4, M Kazmierski3, J Marsilla3, S Huang1,2, S Kim3, K Rey-mcintyre1, B O'Sullivan1,2, J Waldron1,2, N Becker5, S Bratman1,2, A Hope1,2, B Haibe-kains3, T Tadic1,2*, (1) Princess Margaret Cancer Centre, University Health Network, Toronto, ON, CA,(2) Department of Radiation Oncology, University Of Toronto, Toronto, ON, CA, (3) Department of Medical Biophysics, University of Toronto, ON, CA, (4) Techna Institute, University Health Network, Toronto, ON, CA, (5) BCCA - Kelowna, Kelowna, BC, CA
SU-J-BRC-2An Automated Pipeline for AI-Based Analysis of CBCT-Guided Patient Alignment
J Neylon1*, D Luximon1, T Ritter2, J Lamb1, (1) UCLA, Los Angeles, CA, (2) VCU Health System, Chesterfield, VA
TH-F-BRC-1A Geometry-Guided Multi-Beamlet Deep Learning Technique for CT Reconstruction
K Lu1*, L Ren2, F Yin1,3, (1) Duke University, Durham, NC, (2) University of Maryland, Baltimore, MD, (3) Duke University Medical Center, Durham, NC
TU-D1030-IePD-F2-2Deep Learning Method for Objective Segmentation of Radiation-Induced Dermatitis in Skin Photographs
Y Park1*, S Choi2, M Cho2, J Son2, M Han1, J Kim3, H Kim1, D Kim4, J Kim1, C Hong1, (1) Yonsei University College of Medicine, Seoul, , KR, (2) Yongin Severance Hospital, Yongin, ,KR, (3) Yonsei University College of Medicine, Gangnam Severance Hospital, Seoul, Seoul, KR, (4) Yonsei Cancer Center, Seoul, 11, KR,
WE-A-201-6BEST IN PHYSICS (IMAGING): Respiratory Motion Detection and Reconstruction Using CAPTURE and Deep Learning Phase2Phase Network for a 0.35 T MRI-LINAC System
S Chen1*, C Eldeniz2, T Fraum3, D Ludwig4, W Gan5, U Kamilov6, D Yang7, Gach8, H An9, (1) Washington University in St. Louis, Saint Louis, MO, (2) Washington University in St. Louis, Saint Louis, MO, (3) Washington University in St. Louis, Saint Louis, MO, (4) Washington University in St. Louis, Saint Louis, MO, (5) Washington University in St. Louis, Saint Louis, MO, (6) Washington University in St. Louis, St. Louis, MO, (7) Duke University, Chapel Hill, NC, (8) Washington University in St Louis, Saint Louis, MO, (9) Washington University in St. Louis, Saint Louis, MO
WE-B-201-3Fully Automated Segmentation of Prostatic Urethra for MR-Guided Radiation Therapy (MRgRT)
D XU1,2*, T Ma2, R Savjani2, M Cao2, Y Yang2, A Kishan2, F Scalzo3, K Sheng2, (1)Computer Science, University of California, Los Angeles, CA 90095, USA (2) Radiation Oncology, University of California, Los Angeles, CA 90095, USA (3)Computer Science, Pepperdine University, 24255 Pacific Coast Hwy, Los Angeles, CA 90263, USA

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