Zhicheng Ding

The work proposes Displacement-Preserving Relational Distillation (DPRD), which in nnU-Net aligns batch-wise pairwise displacement vectors on ROI-masked case-level embeddings with relational scale normalization, and on ISLES 2022 and AMOS 2022 lets students using about 5% of the teacher's parameters and about 3% of its FLOPs outperform Logits KD, FitNet, RKD, and CIRKD, reaching 85.46% Dice and 11.72 mm HD95 on AMOS.

The work proposes Displacement-Preserving Relational Distillation (DPRD), which in nnU-Net aligns batch-wise pairwise displacement vectors on ROI-masked case-level embeddings with relational scale normalization, and on ISLES 2022 and AMOS 2022 lets students using about 5% of the teacher's parameters and about 3% of its FLOPs outperform Logits KD, FitNet, RKD, and CIRKD, reaching 85.46% Dice and 11.72 mm HD95 on AMOS.