DeepAI AI Chat
Log In Sign Up

Categorical Relation-Preserving Contrastive Knowledge Distillation for Medical Image Classification

by   Xiaohan Xing, et al.
City University of Hong Kong
Southern University of Science & Technology

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.


MED-TEX: Transferring and Explaining Knowledge with Less Data from Pretrained Medical Imaging Models

Deep neural network based image classification methods usually require a...

Complementary Relation Contrastive Distillation

Knowledge distillation aims to transfer representation ability from a te...

DisCo: Effective Knowledge Distillation For Contrastive Learning of Sentence Embeddings

Contrastive learning has been proven suitable for learning sentence embe...

Robust and Efficient Segmentation of Cross-domain Medical Images

Efficient medical image segmentation aims to provide accurate pixel-wise...

Learning Interclass Relations for Image Classification

In standard classification, we typically treat class categories as indep...

Prototype Knowledge Distillation for Medical Segmentation with Missing Modality

Multi-modality medical imaging is crucial in clinical treatment as it ca...