mlsanityData to 14 Oct 2026

Rethinking Bias in Generative Data Augmentation for Medical AI: a Frequency Recalibration Method

arXiv 2511.12301 ↗First posted 15 Nov 2025cs.CV cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Developing Medical AI relies on large datasets and easily suffers from data scarcity. Generative data augmentation (GDA) using AI generative models offers a solution to synthesize realistic medical images. However, the bias in GDA is often underestimated in medical domains, with concerns about the risk of introducing detrimental features generated by AI and harming downstream tasks. This paper identifies the frequency misalignment between real and synthesized images as one of the key factors underlying unreliable GDA and proposes the Frequency Recalibration (FreRec) method to reduce the frequency distributional discrepancy and thus improve GDA. FreRec involves (1) Statistical High-frequency Replacement (SHR) to roughly align high-frequency components and (2) Reconstructive High-frequency Mapping (RHM) to enhance image quality and reconstruct high-frequency details. Extensive experiments were conducted in various medical datasets, including brain MRIs, chest X-rays, and fundus images. The results show that FreRec significantly improves downstream medical image classification performance compared to uncalibrated AI-synthesized samples. FreRec is a standalone post-processing step that is compatible with any generative model and can integrate seamlessly with common medical GDA pipelines.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 36 references that are in the map. First posted on arXiv 15 Nov 2025.

Builds on

18 of 36 references.

References · 18

  1. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
  2. 2018On the Spectral Bias of Neural Networkscited by 1,341
  3. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,186
  4. 2021Alias-Free Generative Adversarial Networkscited by 1,184
  5. 2020Leveraging Frequency Analysis for Deep Fake Image Recognitioncited by 504
  6. 2019High Frequency Component Helps Explain the Generalization of Convolutional Neural Networkscited by 310
  7. 2020Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributionscited by 280
  8. 2021Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domaincited by 244
  9. 2020Focal Frequency Loss for Image Reconstruction and Synthesiscited by 206
  10. 2021Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesiscited by 140
  11. 2019Fourier Spectrum Discrepancies in Deep Network Generated Imagescited by 139
  12. 2023Intriguing properties of synthetic images: from generative adversarial networks to diffusion modelscited by 123
6 more
  1. 2024A Note on Shumailov et al. (2024): `AI Models Collapse When Trained on Recursively Generated Data'cited by 115
  2. 2023Generative models improve fairness of medical classifiers under distribution shiftscited by 90
  3. 2024Is Synthetic Data all We Need? Benchmarking the Robustness of Models Trained with Synthetic Imagescited by 41
  4. 2022StudioGAN: A Taxonomy and Benchmark of GANs for Image Synthesiscited by 38
  5. 2024Fourier-basis Functions to Bridge Augmentation Gap: Rethinking Frequency Augmentation in Image Classificationcited by 18
  6. 2023SynFundus-1M: A High-quality Million-scale Synthetic fundus images Dataset with Fifteen Types of Annotationcited by 5

Built on by

Not cited yet.