# Method and Software Tool for Generating Artificial Databases of Biomedical Images Based on Deep Neural Networks

arXiv: https://arxiv.org/abs/2405.16119 · HTML: https://mlsanity.com/paper/2405.16119/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 25 May 2024.

## Builds on (most cited first, 12 of 12)

- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification](https://mlsanity.com/paper/1803.01229/) (2018)
- [Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge](https://mlsanity.com/paper/1807.08284/) (2018)
- [How to Fool Radiologists with Generative Adversarial Networks? A Visual Turing Test for Lung Cancer Diagnosis](https://mlsanity.com/paper/1710.09762/) (2017)
- [Selective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classification](https://mlsanity.com/paper/2111.06399/) (2021)
- [Learning Implicit Brain MRI Manifolds with Deep Learning](https://mlsanity.com/paper/1801.01847/) (2018)
- [Generating Highly Realistic Images of Skin Lesions with GANs](https://mlsanity.com/paper/1809.01410/) (2018)
- [MelanoGANs: High Resolution Skin Lesion Synthesis with GANs](https://mlsanity.com/paper/1804.04338/) (2018)
- [Unsupervised Histopathology Image Synthesis](https://mlsanity.com/paper/1712.05021/) (2017)
- [Sharp-GAN: Sharpness Loss Regularized GAN for Histopathology Image Synthesis](https://mlsanity.com/paper/2110.14709/) (2021)
- [Unified Framework for Histopathology Image Augmentation and Classification via Generative Models](https://mlsanity.com/paper/2212.09977/) (2022)

## Abstract (arXiv)

A wide variety of biomedical image data, as well as methods for generating training images using basic deep neural networks, were analyzed. Additionally, all platforms for creating images were analyzed, considering their characteristics. The article develops a method for generating artificial biomedical images based on GAN. GAN architecture has been developed for biomedical image synthesis. The data foundation and module for generating training images were designed and implemented in a software system. A comparison of the generated image database with known databases was made.