HBRW: A Hardness-Based Re-Weighting Approach for Long-tailed Medical Image Classification

Traditional image classification methods struggle with the skewed distribution of medical images, where some diseases are overrepresented while others are underrepresented. In light of the diverse learning challenges presented by different medical image samples, we introduce a novel approach to long-tailed medical image classification through a re-weighting strategy based on sample hardness. Our method differs from existing strategies by determining a sample’s weight in the loss function based solely on its hardness - a measure of the sample’s learning difficulty. This approach simplifies the training process, avoids heavy reliance on hyper-parameters, and enhances the model’s generalizability and robustness, especially in the context of long-tailed distributions common in medical image datasets. We validate the effectiveness of our method with comprehensive experiments on the long-tailed medical datasets, demonstrating significant improvements in classification performance compared to existing methods.