# Generative Adversarial Network for Medical Images (MI-GAN)

arXiv: https://arxiv.org/abs/1810.00551 · HTML: https://mlsanity.com/paper/1810.00551/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2018.

## Built on by (most cited first, 17 of 17)

- [Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-art](https://mlsanity.com/paper/2308.16316/) (2023)
- [Adversarial Fine-tuning using Generated Respiratory Sound to Address Class Imbalance](https://mlsanity.com/paper/2311.06480/) (2023)
- [Enhancing Medical Imaging with GANs Synthesizing Realistic Images from Limited Data](https://mlsanity.com/paper/2406.18547/) (2024)
- [Implementation of a Modified U-Net for Medical Image Segmentation on Edge Devices](https://mlsanity.com/paper/2206.02358/) (2022)
- [Generative Adversarial Networks for Synthetic Data Generation: A Comparative Study](https://mlsanity.com/paper/2112.01925/) (2021)
- [Combating COVID-19 using Generative Adversarial Networks and Artificial Intelligence for Medical Images: A Scoping Review](https://mlsanity.com/paper/2205.07236/) (2022)
- [Computer Vision Methods for the Microstructural Analysis of Materials: The State-of-the-art and Future Perspectives](https://mlsanity.com/paper/2208.04149/) (2022)
- [BioGAN: An unpaired GAN-based image to image translation model for microbiological images](https://mlsanity.com/paper/2306.06217/) (2023)
- [Improving diagnosis and prognosis of lung cancer using vision transformers: A scoping review](https://mlsanity.com/paper/2309.02783/) (2023)
- [Brain Tumor Synthetic Data Generation with Adaptive StyleGANs](https://mlsanity.com/paper/2212.01772/) (2022)
- [Cross Modality Medical Image Synthesis for Improving Liver Segmentation](https://mlsanity.com/paper/2503.00945/) (2025)
- [BronchoGAN: Anatomically consistent and domain-agnostic image-to-image translation for video bronchoscopy](https://mlsanity.com/paper/2507.01387/) (2025)
- [Enhancing Diabetic Retinopathy Classification Accuracy through Dual Attention Mechanism in Deep Learning](https://mlsanity.com/paper/2507.19199/) (2025)
- [Structure Unbiased Adversarial Model for Medical Image Segmentation](https://mlsanity.com/paper/2205.12857/) (2022)
- [Application of image-to-image translation in improving pedestrian detection](https://mlsanity.com/paper/2209.03625/) (2022)
- [Dual-path convolutional neural network using micro-FTIR imaging to predict breast cancer subtypes and biomarkers levels: estrogen receptor, progesterone receptor, HER2 and Ki67](https://mlsanity.com/paper/2310.15099/) (2023)
- [DeepVox and SAVE-CT: a contrast- and dose-independent 3D deep learning approach for thoracic aorta segmentation and aneurysm prediction using computed tomography scans](https://mlsanity.com/paper/2310.15328/) (2023)

## Abstract (arXiv)

Deep learning algorithms produces state-of-the-art results for different machine learning and computer vision tasks. To perform well on a given task, these algorithms require large dataset for training. However, deep learning algorithms lack generalization and suffer from over-fitting whenever trained on small dataset, especially when one is dealing with medical images. For supervised image analysis in medical imaging, having image data along with their corresponding annotated ground-truths is costly as well as time consuming since annotations of the data is done by medical experts manually. In this paper, we propose a new Generative Adversarial Network for Medical Imaging (MI-GAN). The MI-GAN generates synthetic medical images and their segmented masks, which can then be used for the application of supervised analysis of medical images. Particularly, we present MI-GAN for synthesis of retinal images. The proposed method generates precise segmented images better than the existing techniques. The proposed model achieves a dice coefficient of 0.837 on STARE dataset and 0.832 on DRIVE dataset which is state-of-the-art performance on both the datasets.