Kaiying Shen

Abstract Multi-phase contrast-enhanced CT (CECT) scans are often scattered across multiple institutions and contain incomplete phase in parts of institutions due to the strict data-protection regulations and the disparity of phase integrality. While federated learning (FL) enables training a privacy-preserving model collaboratively across institutions, it often suffers from significant performance degradation for liver lesion segmentation caused by heterogeneity in different institutions.