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.

翻译：本文提出了一种名为GenMix的新型数据增强技术，该方法融合了生成式与混合增强策略，以充分发挥两者的优势。生成模型虽擅长创造新的数据模式，但在面对有限医学影像数据时，仍面临诸如生成对抗网络中的模式崩溃以及扩散模型训练困难等挑战。另一方面，混合模型虽能增强类别边界区域，但在类别不平衡场景中易偏向主导类别。为克服这些局限性，GenMix将两种方法进行整合以实现优势互补。GenMix包含两个阶段：(1) 训练生成模型以合成图像；(2) 在合成数据与真实数据间执行混合增强。该流程既提升了合成数据的质量与多样性，又能同时受益于生成模型的新模式学习能力与混合模型的边界增强特性。我们在CT图像局灶性肝病变分类任务上验证了所提方法的有效性。实验结果表明，GenMix显著提升了包括DCGAN、StyleGAN、Textual Inversion及扩散模型在内的多种生成模型的性能。值得注意的是，采用Textual Inversion的GenMix方法在未对扩散模型进行FLL数据集微调的情况下，其性能已超越其他对比方法。