# Red blood cell image generation for data augmentation using Conditional Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1901.06219 · HTML: https://mlsanity.com/paper/1901.06219/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2019.

## Built on by (most cited first, 12 of 12)

- [Explainability of deep vision-based autonomous driving systems: Review and challenges](https://mlsanity.com/paper/2101.05307/) (2021)
- [GAN-Based Data Augmentation and Anonymization for Skin-Lesion Analysis: A Critical Review](https://mlsanity.com/paper/2104.10603/) (2021)
- [Implicit Neural Representations for Generative Modeling of Living Cell Shapes](https://mlsanity.com/paper/2207.06283/) (2022)
- [Constrained Adversarial Learning for Automated Software Testing: a literature review](https://mlsanity.com/paper/2303.07546/) (2023)
- [CS2: A Controllable and Simultaneous Synthesizer of Images and Annotations with Minimal Human Intervention](https://mlsanity.com/paper/2206.13394/) (2022)
- [Multi-StyleGAN: Towards Image-Based Simulation of Time-Lapse Live-Cell Microscopy](https://mlsanity.com/paper/2106.08285/) (2021)
- [Generative modeling of living cells with SO(3)-equivariant implicit neural representations](https://mlsanity.com/paper/2304.08960/) (2023)
- [EfficientCellSeg: Efficient Volumetric Cell Segmentation Using Context Aware Pseudocoloring](https://mlsanity.com/paper/2204.03014/) (2022)
- [BioGAN: An unpaired GAN-based image to image translation model for microbiological images](https://mlsanity.com/paper/2306.06217/) (2023)
- [Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2506.23038/) (2025)
- [Using Synthetic Images to Augment Small Medical Image Datasets](https://mlsanity.com/paper/2503.00962/) (2025)
- [Diffusion Model-Based Data Augmentation for Enhanced Neuron Segmentation](https://mlsanity.com/paper/2601.15779/) (2026)

## Abstract (arXiv)

In this paper, we describe how to apply image-to-image translation techniques to medical blood smear data to generate new data samples and meaningfully increase small datasets. Specifically, given the segmentation mask of the microscopy image, we are able to generate photorealistic images of blood cells which are further used alongside real data during the network training for segmentation and object detection tasks. This image data generation approach is based on conditional generative adversarial networks which have proven capabilities to high-quality image synthesis. In addition to synthesizing blood images, we synthesize segmentation mask as well which leads to a diverse variety of generated samples. The effectiveness of the technique is thoroughly analyzed and quantified through a number of experiments on a manually collected and annotated dataset of blood smear taken under a microscope.