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Taxonomy: IM- CT: Machine learning, computer vision
| MO-B-TRACK 6-3 | Predicting PD-L1 Expression Level in Non-Small Cell Lung Cancer On Computed tomography Using Machine Learning T Shiinoki*, K Fujimoto, Y Kawazoe, Y Yuasa, M Kajima, Y Manabe, T Hirano, K Matsunaga, H Tanaka, Yamaguchi University |
| PO-GePV-M-19 | CNN-Based Clinical-Dose to Low-Dose CT Simulator S Tunissen*, J Teuwen, I Sechopoulos, Radboud University Medical Center, Nijmegen, The Netherlands |
| PO-GePV-M-21 | Deep Transfer Learning for the Multi-Label Classification of COVID-19 Presentation On Thoracic CT Scans D Tada*, J Fuhrman, F Li, M Giger, University of Chicago, Chicago, IL |
| PO-GePV-M-22 | Deep Learning–based COVID-19 Pneumonia Classification Using Chest CT Images: Model Generalizability D Nguyen1,2,a*, F Kay3, J Tan2, Y Yan2, Y Ng3, P Iyengar2, R Peshock3, S Jiang1,2,a, (1) Medical Artificial Intelligence and Automation (MAIA) Laboratory, UT Southwestern Medical Center, Dallas, TX, USA, (2) Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA, (3) Department of Radiology, UT Southwestern Medical Center, Dallas, TX, USA, (a) Co-correspondence authors: {Dan.Nguyen, Steve.Jiang}@UTSouthwestern.edu |
| PO-GePV-M-30 | Low-Dose CT Images Denoising Integrating Machine Learning and Optimization Q Xu*, Q Lyu, K Sheng, UCLA School of Medicine, Los Angeles, CA |
| PO-GePV-M-119 | Improvement of MVCT Image Quality for Adaptive Helical Tomotherapy Using CycleGAN-Based Image Synthesis with Small Datasets D Lee*1,3, H Cho1, Y Han2,3, (1) Yonsei University, Wonju, KR, (2) Sungkyunkwan University, Seoul, KR (3) Samsung Medical Center, Seoul, kr |
| PO-GePV-M-127 | DeepDoseNet: A Deep Learning-Based Approach for 3D Dose Prediction M H Soomro1*, V Leandro Alves1, H Nourzadeh2, J Siebers1, (1) University of Virginia Health System, Charlottesville, VA, (2) Thomas Jefferson University, Philadelphia, PA. |
| SU-IePD-TRACK 1-7 | Super-Resolution CT Image Via Convolution Neural Network with An Observer Loss Function M Yu*, M Han, J Baek, Yonsei University, Incheon, 28KR, |
| SU-IePD-TRACK 2-3 | A Deep Learning-Based Radiomics Approach to Identify Patient with Early Tumor Regression Utilizing Planning CT Images for Adaptive Radiotherapy S Tanaka1*, N Kadoya1, Y Sugai1, M Umeda1, Y Katsuta1, K Ito1, T Yamamoto1, N Takahashi1, K Takeda1, S Dobashi2, K Takeda2, K Jingu1, (1) Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan, (2) Department of Radiation Therapy, Tohoku University Graduate School of Medicine, Sendai, Japan |
| SU-IePD-TRACK 3-4 | Ortho2D: An Accurate, Memory-Efficient Framework for 3D Vertebrae Labeling Y Huang1*, A Uneri2, C Jones3, X Zhang4, M Ketcha5, N Aygun6, P Helm7, J Siewerdsen8, (1) Johns Hopkins University, Department of Biomedical Engineering, Baltimore, MD, (2) Johns Hopkins University, Department of Biomedical Engineering, Baltimore, MD, (3) Johns Hopkins University, Department of Computer Science, Baltimore, MD, (4) Johns Hopkins University, Department of Biomedical Engineering, Baltimore, MD, (5) Johns Hopkins University, Department of Biomedical Engineering, Baltimore, MD, (6) Johns Hopkins University, Department of Radiology, Baltimore, MD, (7) Medtronic, Inc., Littelton, MA, (8) Johns Hopkins University, Department of Biomedical Engineering, Baltimore, MD |
| TH-F-TRACK 4-3 | Enabling Few-View 3D Tomographic Image Reconstruction by Geometry-Informed Deep Learning L Shen*, W Zhao, D Capaldi, J Pauly, L Xing, Stanford University, Stanford, CA |
| TU-D-TRACK 3-6 | Task-Based Loss Function for Convolutional Neural Network-Based CT Denoising B Nelson1*, D Gomez Cardona2, N Huber1, A Missert1, L Yu1, C McCollough1, (1) Mayo Clinic, Rochester, MN, (2) Gundersen Health System, La Crosse, WI |
| WE-IePD-TRACK 2-5 | Deep Learning Vs. Iterative Reconstruction in CT Dose Reduction and Image Texture Preservation:A Live Animal Study J Zhang1, F Raslau2, H Ganesh3, E Escott4, J Zhang5*, (1) Math Science Technology Center (mstc), Paul Laurence Dunbar High School, ,,(2) University of Kentucky, Lexington, KY, (3) University Of Kentucky, ,,(4) ,,,(5) University of Kentucky, Lexington, KY |
| WE-IePD-TRACK 5-2 | Multi-Domain Statistical Modeling of Treatment Tolerance in Patients with Gastric and Esophageal Adenocarcinoma A Toronka*, M Defreitas, B Konkel, M Nedrud, I Zaki, A Valentine, S Cubberley, F Yin, M Bashir, K Lafata, Duke University, Durham, NC |