  In this paper, we propose a novel data augmentation technique called GenMix,
which combines generative and mixture approaches to leverage the strengths of
both methods. While generative models excel at creating new data patterns, they
face challenges such as mode collapse in GANs and difficulties in training
diffusion models, especially with limited medical imaging data. On the other
hand, mixture models enhance class boundary regions but tend to favor the major
class in scenarios with class imbalance. To address these limitations, GenMix
integrates both approaches to complement each other. GenMix operates in two
stages: (1) training a generative model to produce synthetic images, and (2)
performing mixup between synthetic and real data. This process improves the
quality and diversity of synthetic data while simultaneously benefiting from
the new pattern learning of generative models and the boundary enhancement of
mixture models. We validate the effectiveness of our method on the task of
classifying focal liver lesions (FLLs) in CT images. Our results demonstrate
that GenMix enhances the performance of various generative models, including
DCGAN, StyleGAN, Textual Inversion, and Diffusion Models. Notably, the proposed
method with Textual Inversion outperforms other methods without fine-tuning
diffusion model on the FLL dataset.
