FoPro-KD: Fourier Prompted Effective Knowledge Distillation for Long-Tailed Medical Image Recognition

Abstract

Transfer learning is a promising technique for medical image classification, particularly for long-tailed datasets. However, the scarcity of data in medical imaging domains often leads to overparameterization when fine-tuning large publicly available pre-trained models. Moreover, these large models are ineffective in deployment in clinical settings due to their computational expenses. To address these challenges, we propose FoPro-KD, a novel approach that unleashes the power of frequency patterns learned from frozen publicly available pre-trained models to enhance their transferability and compression. FoPro-KD comprises three modules: Fourier prompt generator (FPG), effective knowledge distillation (EKD), and adversarial knowledge distillation (AKD). The FPG module learns to generate targeted perturbations conditional on a target dataset, exploring the representations of a frozen pre-trained model, trained on natural images. The EKD module exploits these generalizable representations through distillation to a smaller target model, while the AKD module further enhances the distillation process. Through these modules, FoPro-KD achieves significant improvements in performance on long-tailed medical image classification benchmarks, demonstrating the potential of leveraging the learned frequency patterns from pre-trained models to enhance transfer learning and compression of large pre-trained models for feasible deployment