| Sunday
10:30 AM | SU-A-TRACK 6-1 : Curbing the Errors From Automated Tools Based On An FMEA of An AI-Based Treatment Planning System
K. Nealon*, P. Balter, R. Douglas, D. Fullen, S. Hernandez, P. Nitsch, A. Olanrewaju, M. Soliman, L. Court |
| Sunday
10:37 AM | SU-A-TRACK 6-2 : Generalizability Study of a Fluence Map Prediction Network
L. Ma*, M. Chen, X. Gu, W. Lu |
| Sunday
10:44 AM | SU-A-TRACK 6-3 : Inverse Treatment Planning Using a Virtual Treatment Planner (VTP) for Prostate Cancer Treated with Intensity Modulated Radiation Therapy
D. Sprouts*, C. Shen, Y. Chi |
| Sunday
10:51 AM | SU-A-TRACK 6-4 : Predicting Daily Record of Machine Performance Check Using Multi-Long Short-Term Memory Neural Networks
M. Ma*, J. Dai |
| Sunday
10:58 AM | SU-A-TRACK 6-5 : Towards Automatic Metastasis-Directed Therapy Planning in a Three-Dimensional Beam Model Using Reinforcement Learning
W. Hrinivich*, M. Deek, R. Phillips, H. Li, P. Tran, J. Lee |
| Sunday
11:05 AM | SU-A-TRACK 6-6 : Towards Interpretable Intelligent Automatic Treatment Planning in Radiotherapy: Understanding the Decision-Making Behaviors of a Hierarchical Deep Reinforcement Learning Based Virtual Treatment Planner Network
C. Shen*, L. Chen, X. Jia |
| Sunday
11:12 AM | SU-A-TRACK 6-7 : Treatment Parameters That Impact IROC SRS Phantom Performance Evaluated Using AI
H. Mehrens*, T. Nguyen, S. Edward, S. Hartzell, M. Glenn, D. Branco, N. Hernandez, P. Alvarez, A. Molineu, P. Taylor, S. Kry |
