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|MO-F-TRACK 2-3||Intensity-Based Thresholding of Probability Maps in Deep-Learning-Based Segmentation|
N Bice*, N Kirby, R Li, T Bahr, J Rembish, M Agarwal, S Stathakis, M Fakhreddine, UT Health San Antonio, San Antonio, TX
|PO-GeP-I-225||Using Pattern Recognition to Assess Tumour Perfusion in High Grade Soft Tissue Sarcoma|
D Patel1*, Z Ahmed1, IR Levesque1;2, (1) McGill University, Montreal, QC, CA, (2) Research Institute of the McGill University Health Centre, Montreal, QC, CA
|PO-GeP-M-13||A Deep Transfer Learning-Based Radiomics Model for Prediction of Local Recurrence in Laryngeal Cancer|
Y Jia12*, X Qi2, J Du2, R Chin2, E McKenzie2, K Sheng2, (1) Shaanxi Key Laboratory of Network Data Intelligent Processing; UCLA School of Medicine, Xi'an, Shaanxi, CN, (2) UCLA School of Medicine, Los Angeles, CA
|PO-GeP-M-252||Improved Auto-Segmentation for CT Male Pelvis: Comparison of Deep Learning to Traditional Atlas Segmentation Methods|
C Halley*, H Wan, A Kruzer, D Pittock, D Darkow, M Butler, N Cole, M Bending, P Jacobs, AS Nelson, MIM Software Inc., Cleveland, OH
|PO-GeP-M-439||Comparison of a 3D Convolutional Neural Network Segmentation Method to Traditional Atlas Segmentation for CT Head and Neck Contours|
A Kruzer*, H Wan, M Bending, C Halley, D Darkow, D Pittock, N Cole, P Jacobs, AS Nelson, MIM Software Inc., Cleveland, OH
|PO-GeP-T-559||Learning the Plausible VMAT Subspace with Deep Autoencoders|
N Bice1*, N Kirby1, R Li1, C Kabat1, D Nguyen2, N Papanikolaou1, M Fakhreddine1, (1) UT Health San Antonio, San Antonio, TX, (2) UT Southwestern Medical Center, Dallas, TX.