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

## TL;DR

This paper presents a method using conditional GANs to generate realistic blood cell images and segmentation masks, enhancing small medical datasets for improved segmentation and detection tasks.

## Contribution

The paper introduces a novel application of conditional GANs for generating synthetic blood smear images and masks, aiding data augmentation in medical imaging.

## Key findings

- Generated images are photorealistic and diverse.
- Data augmentation improves segmentation accuracy.
- Method effectively increases dataset size with high-quality samples.

## Abstract

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.