Do Synthetic Brain MRIs Reliably Improve Tumour Classification? A StyleGAN2-ADA Class-Plane Augmentation Study on BRISC 2025
arXiv:2605.23094v1 Announce Type: cross Abstract: Generative augmentation is often proposed as a remedy for small medical-image datasets, but synthetic images are only useful when they improve downstream task performance. "Augmentation" here means synthetic supplementation: GAN-generated samples added to the real training pool, not geometric or photometric transforms of existing images. Twelve class-plane StyleGAN2-ADA generators were trained on constrained BRISC 2025 partitions to test whether their output, with or without InceptionV3 feature-space filtering, improves held-out tumour classifi
The rapid advancement of generative AI models, particularly StyleGAN variants, enables deeper exploration into their utility for data augmentation in specialized fields like medical imaging.
Reliable synthetic data generation can address critical data scarcity issues in medical AI, accelerating diagnostic tool development and reducing reliance on extensive real patient datasets.
The potential to robustly synthetically augment medical imaging datasets could significantly alter AI training methodologies and accessibility in healthcare.
- · AI-driven medical diagnostics
- · Healthcare providers in data-scarce regions
- · Generative AI model developers
- · Traditional data collection methods in medical imaging
- · Diagnostic approaches solely reliant on large real datasets
Improved performance of AI models in medical image classification due to enhanced training data.
Faster development and deployment of new AI diagnostic tools, potentially democratizing access to advanced medical analysis.
Ethical and regulatory frameworks may need to evolve to certify and validate AI systems trained partly on synthetic data, potentially influencing data governance policies.
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Read at arXiv cs.AI