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.

If you haven't, register on our mailing list for more details of this and upcoming events.


@ S-00-010, Building 2, LUMC

  1. Language models in a medical setting

  2. Training large language models