Federated model aggregation methods for image translation in radiotherapy

  • chair:Medical Imaging for Modeling and Simulation
  • type:Master thesis
  • tutor:

    M.Sc. Ciro B. Raggio

  • person in charge:

    B.Sc. Nils Skupien

  • Motivation
    In the last years, image-to-image translation in radiotherapy (Fig.1) has raised increasing interest based on artificial intelligence algorithms such as machine learning or DL. Federated learning is a machine learning approach that enables multiple parties to collaboratively train a model without sharing their data (Fig. 2). This project aims to develop a federated learning framework specifically for translating medical images from one modality to another. By eliminating the need for data centralization, this approach ensures patient data privacy while enabling collaboration between healthcare institutions.
    Within the federated learning context for image-to-image translation in radiotherapy, which federated model aggregation method offers the best performance, considering factors such as privacy, accuracy, and computational efficiency?