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Taxonomy: TH- Response Assessment: Modeling: Machine Learning
|BReP-SNAP-M-7||A Depthwise Separable Convolution Neural Network for Survival Prediction of Head & Neck Cancer|
R Li1*, A Das2, N Bice1, P Rad2, A Roy2, N Kirby1, N Papanikolaou1, (1) University of Texas HSC SA, San Antonio, Texas, (2) The University of Texas at San Antonio
|MO-F-TRACK 2-2||A Deep Learning Based Segmentation and Evaluation Framework for Brain Metastases Follow-Up After Stereotactic Radiosurgery|
Z Yang1*, L Wang2, Y Liu3, M Chen1, E Zhang1, R Timmerman1, T Dan1, Z Wardak1, W Lu1, X Gu1, (1) UT Southwestern Medical Center, Dallas, TX (2) University Of Texas At Arlington, TX (3) Sichuan University, Chengdu, CN
|PO-GeP-M-123||Convolutional Neural Network Learning From RT Dose Distribution and Images Improves Predicting Locoregional Recurrence for Head and Neck Cancer|
A Wu1*, Y Li2, M Qi1, X Lu1, Y Liu1, L Zhou1, T Song1, (1) Southern Medical University, Guangzhou, Guangdong, CN, (2) Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, CN
|TU-CD-TRACK 2-2||Integrating Multi-Omics Information in Deep Learning Architecture for Joint Actuarial Outcome Prediction in Non-Small-Cell Lung Cancer Patients After Radiation Therapy|
S Cui*, R Ten Haken, I El Naqa, University of Michigan, Ann Arbor, MI