Confidence Matters: Enhancing Medical Image Classification Through Uncertainty-Driven Contrastive Self-distillation

The scarcity of data in medical image classification using deep learning often leads to overfitting the training data. Research indicates that self-distillation techniques, particularly those employing mean teacher ensembling, can alleviate this issue. However, directly transferring knowledge distillation (KD) from computer vision to medical image classification yields subpar results due to higher intra-class variance and class imbalance in medical images. This can cause supervised and contrastive learning-based solutions to become biased towards the majority class, resulting in misclassification. To address this, we propose UDCD, an uncertainty-driven contrastive learning-based self-distillation framework that regulates the transfer of contrastive and supervised knowledge, ensuring only relevant knowledge is transferred from the teacher to the student for fine-grained knowledge transfer. By controlling the outcome of the transferable contrastive and teacher's supervised knowledge based on confidence levels, our framework better classifies images under higher intra- and inter-relation constraints with class imbalance raised due to data scarcity, distilling only useful knowledge to the student. Extensive experiments conducted on benchmark datasets such as HAM10000 and APTOS validate the superiority of our proposed method. The code is available at https://github.com/philsaurabh/UDCD_MICCAI.