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 classification across three classifier families: a random forest (RF) on InceptionV₃ features, a compact two-headed convolutional neural network (CNN), and MobileViTV2, a mobile hybrid convolutional–transformer. Each was evaluated at 1:1 and 1:2 real-to-synthetic ratios. An independent GPT-5.5 blind test placed gated real-versus-synthetic discrimination at 57.73% (95% CI: 54.48–60.92%) on the model-legible subset—modestly above chance. The RF classifier did not benefit from the synthetic MRIs. The CNN showed consistent mean gains that did not survive Holm correction. MobileViTV₂ showed the clearest benefit: filtered 1:1 augmentation improved tumour classification accuracy by 1.02% absolute (95% CI: 0.54–1.54%; Holm-corrected p = 0.0104). A secondary efficiency analysis found that every augmented CNN condition selected its checkpoint 42–64% earlier than baseline, while compute-matched MobileViTV₂ runs reached selection after 50–67% fewer real-data epochs. Overall, augmentation utility was found to be architecture- and ratio-dependent, not guaranteed by visual fidelity alone.

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 classification across three classifier families: a random forest (RF) on InceptionV₃ features, a compact two-headed convolutional neural network (CNN), and MobileViTV2, a mobile hybrid convolutional–transformer. Each was evaluated at 1:1 and 1:2 real-to-synthetic ratios. An independent GPT-5.5 blind test placed gated real-versus-synthetic discrimination at 57.73% (95% CI: 54.48–60.92%) on the model-legible subset—modestly above chance. The RF classifier did not benefit from the synthetic MRIs. The CNN showed consistent mean gains that did not survive Holm correction. MobileViTV₂ showed the clearest benefit: filtered 1:1 augmentation improved tumour classification accuracy by 1.02% absolute (95% CI: 0.54–1.54%; Holm-corrected p = 0.0104). A secondary efficiency analysis found that every augmented CNN condition selected its checkpoint 42–64% earlier than baseline, while compute-matched MobileViTV₂ runs reached selection after 50–67% fewer real-data epochs. Overall, augmentation utility was found to be architecture- and ratio-dependent, not guaranteed by visual fidelity alone.

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 classification across three classifier families: a random forest (RF) on InceptionV₃ features, a compact two-headed convolutional neural network (CNN), and MobileViTV2, a mobile hybrid convolutional–transformer. Each was evaluated at 1:1 and 1:2 real-to-synthetic ratios. An independent GPT-5.5 blind test placed gated real-versus-synthetic discrimination at 57.73% (95% CI: 54.48–60.92%) on the model-legible subset—modestly above chance. The RF classifier did not benefit from the synthetic MRIs. The CNN showed consistent mean gains that did not survive Holm correction. MobileViTV₂ showed the clearest benefit: filtered 1:1 augmentation improved tumour classification accuracy by 1.02% absolute (95% CI: 0.54–1.54%; Holm-corrected p = 0.0104). A secondary efficiency analysis found that every augmented CNN condition selected its checkpoint 42–64% earlier than baseline, while compute-matched MobileViTV₂ runs reached selection after 50–67% fewer real-data epochs. Overall, augmentation utility was found to be architecture- and ratio-dependent, not guaranteed by visual fidelity alone.

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 classification across three classifier families: a random forest (RF) on InceptionV₃ features, a compact two-headed convolutional neural network (CNN), and MobileViTV2, a mobile hybrid convolutional–transformer. Each was evaluated at 1:1 and 1:2 real-to-synthetic ratios. An independent GPT-5.5 blind test placed gated real-versus-synthetic discrimination at 57.73% (95% CI: 54.48–60.92%) on the model-legible subset—modestly above chance. The RF classifier did not benefit from the synthetic MRIs. The CNN showed consistent mean gains that did not survive Holm correction. MobileViTV₂ showed the clearest benefit: filtered 1:1 augmentation improved tumour classification accuracy by 1.02% absolute (95% CI: 0.54–1.54%; Holm-corrected p = 0.0104). A secondary efficiency analysis found that every augmented CNN condition selected its checkpoint 42–64% earlier than baseline, while compute-matched MobileViTV₂ runs reached selection after 50–67% fewer real-data epochs. Overall, augmentation utility was found to be architecture- and ratio-dependent, not guaranteed by visual fidelity alone.