A privacy-preserving federated learning framework for generalizable CBCT to synthetic CT translation in head and neck - Summary - MDSpire

A privacy-preserving federated learning framework for generalizable CBCT to synthetic CT translation in head and neck

  • By

  • Ciro Benito Raggio

  • Paolo Zaffino

  • Maria Francesca Spadea

  • June 15, 2026

  • 0 min

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Objective:

To propose a federated learning approach for synthesizing synthetic CT from cone-beam computed tomography while preserving data privacy across multiple institutions.

Key Findings:
  • Include specific numerical values for MAE, SSIM, and PSNR as mentioned in the source.
Interpretation:

Remove this section as it adds unsupported conclusions.

Limitations:
  • 1
Conclusion:

Revise to avoid implications of collaboration without data sharing unless directly supported by the source.

Original Source(s)

A privacy-preserving federated learning framework for generalizable CBCT to synthetic CT translation in head and neck

  • by Ciro Benito Raggio, Paolo Zaffino, Maria Francesca Spadea

  • June 15, 2026

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