Categorical Relation-Preserving Contrastive Knowledge Distillation for Medical Image Classification

TL;DR

This paper introduces CRCKD, a novel knowledge distillation method for medical image classification that enhances intra-class similarity and inter-class separation, addressing challenges of limited data and class imbalance.

Contribution

The paper proposes a new CRCKD algorithm with CCD and CRP modules, improving relational knowledge transfer in medical image classification.

Findings

CRCKD outperforms existing methods on HAM10000 and APTOS datasets.

The CCD module effectively pulls same-class features closer.

The CRP loss preserves relational knowledge in a class-balanced way.

Abstract

The amount of medical images for training deep classification models is typically very scarce, making these deep models prone to overfit the training data. Studies showed that knowledge distillation (KD), especially the mean-teacher framework which is more robust to perturbations, can help mitigate the over-fitting effect. However, directly transferring KD from computer vision to medical image classification yields inferior performance as medical images suffer from higher intra-class variance and class imbalance. To address these issues, we propose a novel Categorical Relation-preserving Contrastive Knowledge Distillation (CRCKD) algorithm, which takes the commonly used mean-teacher model as the supervisor. Specifically, we propose a novel Class-guided Contrastive Distillation (CCD) module to pull closer positive image pairs from the same class in the teacher and student models, while pushing apart negative image pairs from different classes. With this regularization, the feature distribution of the student model shows higher intra-class similarity and inter-class variance. Besides, we propose a Categorical Relation Preserving (CRP) loss to distill the teacher's relational knowledge in a robust and class-balanced manner. With the contribution of the CCD and CRP, our CRCKD algorithm can distill the relational knowledge more comprehensively. Extensive experiments on the HAM10000 and APTOS datasets demonstrate the superiority of the proposed CRCKD method.