Probability maps for deep learning-based head and neck tumor segmentation: Graphical User Interface design and test

This publication has 9 references indexed in Scilit:

• Deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for predicted tumor probability in FDG PET and CT images
  Physics in Medicine & Biology, 2023
• Uncertainty Assessment for Deep Learning Radiotherapy Applications
  Seminars in Radiation Oncology, 2022
• Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency
  Physics in Medicine & Biology, 2022
• Head and neck tumor segmentation in PET/CT: The HECKTOR challenge
  Medical Image Analysis, 2022
• Clinical implementation of deep-learning based auto-contouring tools–Experience of three French radiotherapy centers
  Cancer/Radiothérapie, 2021
• A Review of Explainable Deep Learning Cancer Detection Models in Medical Imaging
  Applied Sciences, 2021
• Tumor delineation: The weakest link in the search for accuracy in radiotherapy
  Journal of Medical Physics, 2008
• Target Definition in Prostate, Head, and Neck
  Seminars in Radiation Oncology, 2005
• IBM computer usability satisfaction questionnaires: Psychometric evaluation and instructions for use
  International Journal of Human–Computer Interaction, 1995