# Enhancing Medical Imaging with GANs Synthesizing Realistic Images from Limited Data

arXiv: https://arxiv.org/abs/2406.18547 · HTML: https://mlsanity.com/paper/2406.18547/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 28 references that are in the map.
- First posted on arXiv 22 May 2024.

## Builds on (most cited first, 16 of 16)

- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Deep MR to CT Synthesis using Unpaired Data](https://mlsanity.com/paper/1708.01155/) (2017)
- [Survival Prediction Across Diverse Cancer Types Using Neural Networks](https://mlsanity.com/paper/2404.08713/) (2024)
- [Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networks](https://mlsanity.com/paper/2311.10944/) (2023)
- [Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example](https://mlsanity.com/paper/2404.08279/) (2024)
- [Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation](https://mlsanity.com/paper/2306.04811/) (2023)
- [Exploring Diverse Methods in Visual Question Answering](https://mlsanity.com/paper/2404.13565/) (2024)
- [Improving Depth Gradient Continuity in Transformers: A Comparative Study on Monocular Depth Estimation with CNN](https://mlsanity.com/paper/2308.08333/) (2023)
- [Regional Style and Color Transfer](https://mlsanity.com/paper/2404.13880/) (2024)
- [3D-Aware Neural Body Fitting for Occlusion Robust 3D Human Pose Estimation](https://mlsanity.com/paper/2308.10123/) (2023)
- [An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Dataset](https://mlsanity.com/paper/2110.00715/) (2021)
- [Generative Adversarial Network for Medical Images (MI-GAN)](https://mlsanity.com/paper/1810.00551/) (2018)
- [Constrained Attractor Selection Using Deep Reinforcement Learning](https://mlsanity.com/paper/1909.10500/) (2019)
- [Attractor Selection in Nonlinear Energy Harvesting Using Deep Reinforcement Learning](https://mlsanity.com/paper/2010.01255/) (2020)
- [DF-DM: A foundational process model for multimodal data fusion in the artificial intelligence era](https://mlsanity.com/paper/2404.12278/) (2024)
- [CT-SRCNN: Cascade Trained and Trimmed Deep Convolutional Neural Networks for Image Super Resolution](https://mlsanity.com/paper/1711.04048/) (2017)

## Built on by (most cited first, 11 of 11)

- [Dynamic Hypergraph-Enhanced Prediction of Sequential Medical Visits](https://mlsanity.com/paper/2408.07084/) (2024)
- [A Lightweight GAN-Based Image Fusion Algorithm for Visible and Infrared Images](https://mlsanity.com/paper/2409.15332/) (2024)
- [Wasserstein Distance-Weighted Adversarial Network for Cross-Domain Credit Risk Assessment](https://mlsanity.com/paper/2409.18544/) (2024)
- [Text classification optimization algorithm based on graph neural network](https://mlsanity.com/paper/2408.15257/) (2024)
- [Convolutional Neural Networks for Predictive Modeling of Lung Disease](https://mlsanity.com/paper/2408.12605/) (2024)
- [Reducing Bias in Deep Learning Optimization: The RSGDM Approach](https://mlsanity.com/paper/2409.15314/) (2024)
- [Optimizing News Text Classification with Bi-LSTM and Attention Mechanism for Efficient Data Processing](https://mlsanity.com/paper/2409.15576/) (2024)
- [Deep Learning-Based Channel Squeeze U-Structure for Lung Nodule Detection and Segmentation](https://mlsanity.com/paper/2409.13868/) (2024)
- [Stock Type Prediction Model Based on Hierarchical Graph Neural Network](https://mlsanity.com/paper/2412.06862/) (2024)
- [Patient-Zero: Scaling Synthetic Patient Agents to Real-World Distributions without Real Patient Data](https://mlsanity.com/paper/2509.11078/) (2025)
- [Leveraging Modified Ex Situ Tomography Data for Segmentation of In Situ Synchrotron X-Ray Computed Tomography](https://mlsanity.com/paper/2504.19200/) (2025)

## Abstract (arXiv)

In this research, we introduce an innovative method for synthesizing medical images using generative adversarial networks (GANs). Our proposed GANs method demonstrates the capability to produce realistic synthetic images even when trained on a limited quantity of real medical image data, showcasing commendable generalization prowess. To achieve this, we devised a generator and discriminator network architecture founded on deep convolutional neural networks (CNNs), leveraging the adversarial training paradigm for model optimization. Through extensive experimentation across diverse medical image datasets, our method exhibits robust performance, consistently generating synthetic images that closely emulate the structural and textural attributes of authentic medical images.