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Multi-Disciplinary: Data Science/RadiomicsMonday - 7/26/2021
Multi-Disciplinary Interactive ePoster Discussion3:00 PM - 3:30 PMTRACK 4
Monday
3:00 PM - 3:30 PM
MO-IePD-TRACK 4-1 : Evaluation of Cone-Beam Computed Tomography-Based Radiomic Features Reproducibility: A Phantom Study
T. Adachi*, M. Nakamura, H. Iramina, T. Mizowaki
Monday
3:00 PM - 3:30 PM
MO-IePD-TRACK 4-2 : Graph Theory-Based Radiomics Features: Application of Tumor Network Structures On CT-Based Radiomics for Prognostic Prediction
M. Umeda*, N. Kadoya, S. Tanaka, S. Tanabe, Y. Sugai, T. Ishida, H. Ohashi, S. Dobashi, K. Takeda, K. Jingu
Monday
3:00 PM - 3:30 PM
MO-IePD-TRACK 4-3 : Explainable AI Model for COVID-19 Diagnosis Through Joint Deep Learning and Radiomics
D. Yang*, G. Ren, M. Ying, J. Cai
Monday
3:00 PM - 3:30 PM
MO-IePD-TRACK 4-4 : Treatment Response Prediction for MRI-Guided Adaptive Radiation Therapy of Pancreatic Cancer Using Multiscale Wavelet-Based Delta-Radiomics
H. Nasief*, W. Hall, X. Chen, E. Paulson, B. Erickson, X. Li
Monday
3:00 PM - 3:30 PM
MO-IePD-TRACK 4-5 : Exploratory Unsupervised Structure-Learning Based Radiomics Approach for Brain Metastases Treatment Response Modeling of Stereotactic Radiosurgery
Z. Yang*, L. Wang, M. Chen, R. Timmerman, T. Dan, Z. Wardak, W. Lu, X. Gu
Monday
3:00 PM - 3:30 PM
MO-IePD-TRACK 4-6 : CT-Based Deep Learning Radiomics for Predicting Chemoradiation Treatment Response in Locally Advanced Rectal Cancer
J. Fu*, Z. Wang, K. Singhrao, J. Lewis, X. Qi
Monday
3:00 PM - 3:30 PM
MO-IePD-TRACK 4-7 : Can Unified Data Improve the Performance of Radiomics-Based Prognostic Prediction in Lung Cancer Patients?
Y. Sugai*, N. Kadoya, S. Tanaka, S. Tanabe, M. Umeda, T. Yamamoto, K. Takeda, S. Dobashi, H. Ohashi, K. Takeda, K. Jingu

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