# Red-GAN: Attacking class imbalance via conditioned generation. Yet another perspective on medical image synthesis for skin lesion dermoscopy and brain tumor MRI

arXiv: https://arxiv.org/abs/2004.10734 · HTML: https://mlsanity.com/paper/2004.10734/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2020.

## Built on by (most cited first, 17 of 17)

- [Conditional Diffusion Models for Semantic 3D Brain MRI Synthesis](https://mlsanity.com/paper/2305.18453/) (2023)
- [Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review](https://mlsanity.com/paper/2307.13125/) (2023)
- [Can segmentation models be trained with fully synthetically generated data?](https://mlsanity.com/paper/2209.08256/) (2022)
- [DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathology](https://mlsanity.com/paper/2306.13384/) (2023)
- [A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysis](https://mlsanity.com/paper/2201.07646/) (2022)
- [GAN-based Data Augmentation for Chest X-ray Classification](https://mlsanity.com/paper/2107.02970/) (2021)
- [Synthetically Enhanced: Unveiling Synthetic Data's Potential in Medical Imaging Research](https://mlsanity.com/paper/2311.09402/) (2023)
- [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021)
- [METGAN: Generative Tumour Inpainting and Modality Synthesis in Light Sheet Microscopy](https://mlsanity.com/paper/2104.10993/) (2021)
- [A Unified Framework for Generative Data Augmentation: A Comprehensive Survey](https://mlsanity.com/paper/2310.00277/) (2023)
- [A 3D generative model of pathological multi-modal MR images and segmentations](https://mlsanity.com/paper/2311.04552/) (2023)
- [A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning](https://mlsanity.com/paper/2203.00624/) (2022)
- [Convolutional Ensembling based Few-Shot Defect Detection Technique](https://mlsanity.com/paper/2208.03288/) (2022)
- [A Domain-specific Perceptual Metric via Contrastive Self-supervised Representation: Applications on Natural and Medical Images](https://mlsanity.com/paper/2212.01577/) (2022)
- [Synthetic Generation of Dermatoscopic Images with GAN and Closed-Form Factorization](https://mlsanity.com/paper/2410.05114/) (2024)
- [Diff4MMLiTS: Advanced Multimodal Liver Tumor Segmentation via Diffusion-Based Image Synthesis and Alignment](https://mlsanity.com/paper/2412.20418/) (2024)
- [Automated analysis of diabetic retinopathy using vessel segmentation maps as inductive bias](https://mlsanity.com/paper/2210.16053/) (2022)

## Abstract (arXiv)

Exploiting learning algorithms under scarce data regimes is a limitation and a reality of the medical imaging field. In an attempt to mitigate the problem, we propose a data augmentation protocol based on generative adversarial networks. We condition the networks at a pixel-level (segmentation mask) and at a global-level information (acquisition environment or lesion type). Such conditioning provides immediate access to the image-label pairs while controlling global class specific appearance of the synthesized images. To stimulate synthesis of the features relevant for the segmentation task, an additional passive player in a form of segmentor is introduced into the adversarial game. We validate the approach on two medical datasets: BraTS, ISIC. By controlling the class distribution through injection of synthetic images into the training set we achieve control over the accuracy levels of the datasets' classes.