4 papers
Energy-Efficient Federated Learning via Adaptive Encoder Freezing for MRI-to-CT Conversion: A Green AI-Guided Research
Ciro Benito Raggio, Lucia Migliorelli, Nils Skupien +6
Federated Learning (FL) holds the potential to advance equality in health by enabling diverse institutions to collaboratively train deep learning (DL) models, even with limited dat…
A Privacy-Preserving Federated Learning Framework for Generalizable CBCT to Synthetic CT Translation in Head and Neck
Ciro Benito Raggio, Paolo Zaffino, Maria Francesca Spadea
Shortened Abstract Cone-beam computed tomography (CBCT) has become a widely adopted modality for image-guided radiotherapy (IGRT). However, CBCT suffers from increased noise, limit…
FedSynthCT-Brain: A Federated Learning Framework for Multi-Institutional Brain MRI-to-CT Synthesis
Ciro Benito Raggio, Mathias Krohmer Zabaleta, Nils Skupien +6
The generation of Synthetic Computed Tomography (sCT) images has become a pivotal methodology in modern clinical practice, particularly in the context of Radiotherapy (RT) treatmen…
Deep learning-based synthetic-CT generation in radiotherapy and PET: a review
Maria Francesca Spadea, Matteo Maspero, Paolo Zaffino +1
Recently, deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical one…