Comparing Bayesian models for organ contouring in head and neck radiotherapy

@inproceedings{Mody2022ComparingBM,
  title={Comparing Bayesian models for organ contouring in head and neck radiotherapy},
  author={Prerak Mody and Nicolas F. Chaves-de-Plaza and Klaus Hildebrandt and Ren{\'e} van Egmond and Huib de Ridder and Marius Staring},
  booktitle={Medical Imaging},
  year={2022}
}
Deep learning models for organ contouring in radiotherapy are poised for clinical usage, but currently, there exist few tools for automated quality assessment (QA) of the predicted contours. Bayesian models and their associated uncertainty, can potentially automate the process of detecting inaccurate predictions. We investigate two Bayesian models for auto-contouring, DropOut and FlipOut, using a quantitative measure – expected calibration error (ECE) and a qualitative measure – region-based… 

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