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Dose-Aware and Robust Contour QA for Radiotherapy 0.1 documentation
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Contents
1. Introduction
2. Background
Dosimetric Awareness of Radiation Oncology Professionals
Predicting the Impact of Target Volume Contouring Variations on the Organ at Risk Dose: Results of a Qualitative Survey
Comparing the Performance of Radiation Oncologists versus a Deep Learning Dose Predictor to Estimate Dosimetric Impact of Segmentation Variations for Radiotherapy
AutoDoseRank: Automated Dosimetry-Informed Segmentation Ranking for Radiotherapy
Sensitivity of Dose Prediction Models
Deep-learning-based dose predictor for glioblastoma–assessing the sensitivity and robustness for dose awareness in contouring
How sensitive are Deep Learning based Radiotherapy Dose Prediction Models to Variability in Organs at Risk Segmentation?
ASTRA: Atomic Surface Transformations for Radiotherapy Quality Assurance
Robustness of Segmentation Models
The impact of U-Net architecture choices and skip connections on the robustness of segmentation across texture variations
Do We Really Need that Skip-Connection? Understanding Its Interplay with Task Complexity
How do 3D image segmentation networks behave across the context versus foreground ratio trade-off?
12. Conclusions
References
Index