Privacy-preserving federated transfer learning for enhanced liver lesion segmentation in PET-CT imaging.

Abstract

Positron Emission Tomography-Computed Tomography (PET-CT) evolution is critical for liver lesion diagnosis. However, data scarcity, privacy concerns, and cross-institutional imaging heterogeneity impede accurate deep learning model deployment. We propose a Federated Transfer Learning (FTL) framework that integrates federated learning's privacy-preserving collaboration with transfer learning's pre-trained model adaptation, enhancing liver lesion segmentation in PET-CT imaging. By leveraging a Feature Co-learning Block (FCB) and privacy-enhancing technologies (DP, HE), our approach ensures robust segmentation without sharing sensitive patient data. (1) A privacy-preserving FTL framework combining federated learning and adaptive transfer learning; (2) A multi-modal FCB for improved PET-CT feature integration; (3) Extensive evaluation across diverse institutions with privacy-enhancing technologies like Differential Privacy (DP) and Homomorphic Encryption (HE). Experiments on simulated multi-institutional PET-CT datasets demonstrate superior performance compared to baselines, with robust privacy guarantees. The FTL framework reduces data requirements and enhances generalizability, advancing liver lesion diagnostics.