Data Augmentation For Medical MR Image Using Generative Adversarial Networks
Not on the map. Why?
Abstract (arXiv)
Computer-assisted diagnosis (CAD) based on deep learning has become a crucial diagnostic technology in the medical industry, effectively improving diagnosis accuracy. However, the scarcity of brain tumor Magnetic Resonance (MR) image datasets causes the low performance of deep learning algorithms. The distribution of transformed images generated by traditional data augmentation (DA) intrinsically resembles the original ones, resulting in a limited performance in terms of generalization ability. This work improves Progressive Growing of GANs with a structural similarity loss function (PGGAN-SSIM) to solve image blurriness problems and model collapse. We also explore other GAN-based data augmentation to demonstrate the effectiveness of the proposed model. Our results show that PGGAN-SSIM successfully generates 256x256 realistic brain tumor MR images which fill the real image distribution uncovered by the original dataset. Furthermore, PGGAN-SSIM exceeds other GAN-based methods, achieving promising performance improvement in Frechet Inception Distance (FID) and Multi-scale Structural Similarity (MS-SSIM).
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 32 references that are in the map. First posted on arXiv 29 Nov 2021.
Builds on
17 of 32 references.
References · 17
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
- 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,988
- 2017Improved Training of Wasserstein GANscited by 2,235
- 2016Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Networkcited by 1,204
- 2017Towards Principled Methods for Training Generative Adversarial Networkscited by 464
- 2017Deep Learning for Medical Image Analysiscited by 401
- 2018Generative Adversarial Network in Medical Imaging: A Reviewcited by 257
- 2015Brain Tumor Segmentation with Deep Neural Networkscited by 137
- 2018Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networkscited by 132
- 2016Medical Image Synthesis with Context-Aware Generative Adversarial Networkscited by 91
- 2018GANs for Medical Image Analysiscited by 81
5 more
- 2019Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networkscited by 65
- 2017Variational Approaches for Auto-Encoding Generative Adversarial Networkscited by 52
- 2018Deep Adversarial Context-Aware Landmark Detection for Ultrasound Imagingcited by 4
- 2018Generative adversarial networks and adversarial methods in biomedical image analysiscited by 2
- 2020Backpropagating through Fr'echet Inception Distancecited by 2
Built on by
Citing papers per year: 2023: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
- 2023Generating 3D Brain Tumor Regions in MRI using Vector-Quantization Generative Adversarial Networks
- 2023Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review
- 2023Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion models
- 2024Addressing Small and Imbalanced Medical Image Datasets Using Generative Models: A Comparative Study of DDPM and PGGANs with Random and Greedy K Sampling
- 2022Multitask Brain Tumor Inpainting with Diffusion Models: A Methodological Report
- 2025Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translation
- 2025Comparative Analysis of GAN and Diffusion for MRI-to-CT translation
- 2025Benchmarking GANs, Diffusion Models, and Flow Matching for T1w-to-T2w MRI Translation