Synthetic Data Generation for Long-Tail Medical Image Classification: A Case Study in Skin Lesions

TL;DR

This paper presents a diffusion-model-based synthetic data augmentation method to improve long-tail skin lesion classification, significantly boosting performance on rare classes in imbalanced medical datasets.

Contribution

The authors introduce a novel diffusion inpainting model with OOD post-selection for diverse, realistic synthetic medical images, enhancing long-tail classification performance.

Findings

Achieved over 28% improvement on the rarest class in skin lesion dataset.

Significant overall performance gains demonstrated on imbalanced medical imaging data.

Diffusion-based augmentation outperforms existing methods in class imbalance scenarios.

Abstract

Long-tailed class distributions are pervasive in multi-class medical datasets and pose significant challenges for deep learning models which typically underperform on tail classes with limited samples. This limitation is particularly problematic in medical applications, where rare classes often correspond to severe or high-risk diseases and therefore require high diagnostic accuracy. Existing solutions-including specialized architectures, rebalanced loss functions, and handcrafted data augmentation-offer only marginal improvements and struggle to scale due to their limited and largely deterministic variability. To address these challenges, we introduce a diffusion-model-driven synthetic data augmentation pipeline tailored for medical long-tailed classification. Our approach features a novel inpainting diffusion model combined with an Out-of-Distribution (OOD) post-selection mechanism to ensure diverse, realistic, and clinically meaningful synthetic samples. Evaluated on the ISIC2019 skin lesion classification dataset, one of the largest and most imbalanced medical imaging benchmarks, our method yields substantial improvements in overall performance, with particularly pronounced gains on tail classes with more than $28\%$ improvement on the class with the fewest samples. These results demonstrate the effectiveness of diffusion-based augmentation in mitigating long-tail imbalance and enhancing medical classification robustness.