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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 |