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  1. Towards clinically acceptable deep learning-based auto-contouring for brachytherapy of cervical cancer

    How important is the architecture of a deep learning model used for auto-contouring in brachytherapy? What challenges do the data bring us? Is it possible to achieve clinically acceptable results, and if yes, how?.

  2. Deep learning-based automated radiotherapy planning validation for oropharyngeal cancer patients

    Radiotherapy treatment planning for head and neck cancer (HNC) is a labor-intensive process which can take up to a day per patient. Additionally, plan quality is highly dependent on the experience of the treatment planner. We explore the performance of deep learning-based autoplanning in RayStation, which claims to generate clinically acceptable and deliverable treatment plans within 15 minutes.

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@ S-00-010, Building 2, LUMC

  1. Language models in a medical setting

  2. Training large language models