GeMix: Conditional GAN-Based Mixup for Improved Medical Image Augmentation

• URL: http://arxiv.org/abs/2507.15577v1
• Date: Mon, 21 Jul 2025 12:58:05 GMT
• Title: GeMix: Conditional GAN-Based Mixup for Improved Medical Image Augmentation
• Authors: Hugo Carlesso, Maria Eliza Patulea, Moncef Garouani, Radu Tudor Ionescu, Josiane Mothe,
• Abstract summary: We propose GeMix, a two-stage framework that replaces blending with a learned, label-aware powered by class-conditional GANs.<n>We benchmark GeMix on the large-scale COVIDx-CT-3 dataset using three backbones (ResNet-50, ResNet-101, EfficientNet-B0). When combined with real data, our method increases macro-F1 over traditional mixup for all backbones, reducing the false negative rate for COVID-19 detection.
• Score: 20.792833454137625
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mixup has become a popular augmentation strategy for image classification, yet its naive pixel-wise interpolation often produces unrealistic images that can hinder learning, particularly in high-stakes medical applications. We propose GeMix, a two-stage framework that replaces heuristic blending with a learned, label-aware interpolation powered by class-conditional GANs. First, a StyleGAN2-ADA generator is trained on the target dataset. During augmentation, we sample two label vectors from Dirichlet priors biased toward different classes and blend them via a Beta-distributed coefficient. Then, we condition the generator on this soft label to synthesize visually coherent images that lie along a continuous class manifold. We benchmark GeMix on the large-scale COVIDx-CT-3 dataset using three backbones (ResNet-50, ResNet-101, EfficientNet-B0). When combined with real data, our method increases macro-F1 over traditional mixup for all backbones, reducing the false negative rate for COVID-19 detection. GeMix is thus a drop-in replacement for pixel-space mixup, delivering stronger regularization and greater semantic fidelity, without disrupting existing training pipelines. We publicly release our code at https://github.com/hugocarlesso/GeMix to foster reproducibility and further research.

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