Feature distance-weighted adaptive decoupled knowledge distillation for medical image segmentation.

Purpose: This paper aims to apply decoupled knowledge distillation (DKD) to medical image segmentation, focusing on transferring knowledge from a high-performance teacher network to a lightweight student network, thereby facilitating model deployment on embedded devices.

Methods: We initially decouple the distillation loss into pixel-wise target class knowledge distillation (PTCKD) and pixel-wise non-target class knowledge distillation (PNCKD). Subsequently, to address the limitations of the fixed weight paradigm in PTCKD, we propose a novel feature distance-weighted adaptive decoupled knowledge distillation (FDWA-DKD) method. FDWA-DKD quantifies the feature disparity between student and teacher, generating instance-level adaptive weights for PTCKD. We design a feature distance weighting (FDW) module that dynamically calculates feature distance to obtain adaptive weights, integrating feature space distance information into logit distillation. Lastly, we introduce a class-wise feature probability distribution loss to encourage the student to mimic the teacher's spatial distribution.

Results: Extensive experiments conducted on the Synapse and FLARE22 datasets demonstrate that our proposed FDWA-DKD achieves satisfactory performance, yielding optimal Dice scores and, in some instances, surpassing the performance of the teacher network. Ablation studies further validate the effectiveness of each module within our proposed method.

Conclusion: Our method overcomes the constraints of traditional distillation methods by offering instance-level adaptive learning weights tailored to PTCKD. By quantifying student-teacher feature disparity and minimizing class-wise feature probability distribution loss, our method outperforms other distillation methods.