# GAN-GA: A Generative Model based on Genetic Algorithm for Medical Image Generation

arXiv: https://arxiv.org/abs/2401.00314 · HTML: https://mlsanity.com/paper/2401.00314/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 30 Dec 2023.

## Builds on (most cited first, 10 of 10)

- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [Generative Adversarial Network in Medical Imaging: A Review](https://mlsanity.com/paper/1809.07294/) (2018)
- [X2CT-GAN: Reconstructing CT from Biplanar X-Rays with Generative Adversarial Networks](https://mlsanity.com/paper/1905.06902/) (2019)
- [Dual Discriminator Generative Adversarial Nets](https://mlsanity.com/paper/1709.03831/) (2017)
- [Normalization of breast MRIs using Cycle-Consistent Generative Adversarial Networks](https://mlsanity.com/paper/1912.08061/) (2019)
- [EvoGAN: An Evolutionary Computation Assisted GAN](https://mlsanity.com/paper/2110.11583/) (2021)
- [CNN-Based Automatic Urinary Particles Recognition](https://mlsanity.com/paper/1803.02699/) (2018)
- [Feeding the zombies: Synthesizing brain volumes using a 3D progressive growing GAN](https://mlsanity.com/paper/1912.05357/) (2019)
- [FFusionCGAN: An end-to-end fusion method for few-focus images using conditional GAN in cytopathological digital slides](https://mlsanity.com/paper/2001.00692/) (2020)

## Built on by (most cited first, 1 of 1)

- [CAT: Contrastive Adapter Training for Personalized Image Generation](https://mlsanity.com/paper/2404.07554/) (2024)

## Abstract (arXiv)

Medical imaging is an essential tool for diagnosing and treating diseases. However, lacking medical images can lead to inaccurate diagnoses and ineffective treatments. Generative models offer a promising solution for addressing medical image shortage problems due to their ability to generate new data from existing datasets and detect anomalies in this data. Data augmentation with position augmentation methods like scaling, cropping, flipping, padding, rotation, and translation could lead to more overfitting in domains with little data, such as medical image data. This paper proposes the GAN-GA, a generative model optimized by embedding a genetic algorithm. The proposed model enhances image fidelity and diversity while preserving distinctive features. The proposed medical image synthesis approach improves the quality and fidelity of medical images, an essential aspect of image interpretation. To evaluate synthesized images: Frechet Inception Distance (FID) is used. The proposed GAN-GA model is tested by generating Acute lymphoblastic leukemia (ALL) medical images, an image dataset, and is the first time to be used in generative models. Our results were compared to those of InfoGAN as a baseline model. The experimental results show that the proposed optimized GAN-GA enhances FID scores by about 6.8%, especially in earlier training epochs. The source code and dataset will be available at: https://github.com/Mustafa-AbdulRazek/InfoGAN-GA.