Multi-Dataset Cross-Domain Knowledge Distillation for Medical Image Segmentation

We propose a novel cross-domain transfer learning framework that leverages knowledge from multiple datasets to improve medical image segmentation on a target task. Our method employs a teacher-student learning paradigm, where a joint teacher model aggregates domain-invariant features from diverse datasets, and a dataset-specific student model is trained via knowledge distillation. We validate our approach on six medical imaging datasets —BrainMetShare, ISLES, BraTS (MRI-based) and Lung MSD, LiTS, KiTS (CT-based)— demonstrating its effectiveness in addressing distributional shifts and enhancing segmentation accuracy across heterogeneous tasks. The results show consistent improvements over baseline models. These findings underscore the potential of multi-dataset knowledge distillation for robust and generalizable segmentation in medical imaging applications.