# Exploring Variational Autoencoders for Medical Image Generation: A Comprehensive Study

arXiv: https://arxiv.org/abs/2411.07348 · HTML: https://mlsanity.com/paper/2411.07348/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2024.

## Built on by (most cited first, 9 of 9)

- [Enhancing Medical Image Analysis through Geometric and Photometric transformations](https://mlsanity.com/paper/2501.13643/) (2025)
- [RetinaRegen: A Hybrid Model for Readability and Detail Restoration in Fundus Images](https://mlsanity.com/paper/2502.19153/) (2025)
- [Latent Space Analysis for Interpretable Uncertainty in Melanoma Classification](https://mlsanity.com/paper/2506.18414/) (2025)
- [Interpretable Prediction of Lymph Node Metastasis in Rectal Cancer MRI Using Variational Autoencoders](https://mlsanity.com/paper/2507.11638/) (2025)
- [CSEval: A Framework for Evaluating Clinical Semantics in Text-to-Image Generation](https://mlsanity.com/paper/2602.12004/) (2026)
- [Data-driven Synthesis of Magnetic Resonance Spectroscopy Data using a Variational Autoencoder](https://mlsanity.com/paper/2603.00736/) (2026)
- [HUydra: Full-Range Lung CT Synthesis via Multiple HU Interval Generative Modelling](https://mlsanity.com/paper/2603.23041/) (2026)
- [Structural MRI Synthesis for Alzheimer's Disease via Conditional Diffusion on Anatomical Masks](https://mlsanity.com/paper/2606.18354/) (2026)
- [Diffusion-Based Tumor Inpainting for Renal Segmentation under Clinical Data Scarcity](https://mlsanity.com/paper/2609.21698/) (2026)

## Abstract (arXiv)

Variational autoencoder (VAE) is one of the most common techniques in the field of medical image generation, where this architecture has shown advanced researchers in recent years and has developed into various architectures. VAE has advantages including improving datasets by adding samples in smaller datasets and in datasets with imbalanced classes, and this is how data augmentation works. This paper provides a comprehensive review of studies on VAE in medical imaging, with a special focus on their ability to create synthetic images close to real data so that they can be used for data augmentation. This study reviews important architectures and methods used to develop VAEs for medical images and provides a comparison with other generative models such as GANs on issues such as image quality, and low diversity of generated samples. We discuss recent developments and applications in several medical fields highlighting the ability of VAEs to improve segmentation and classification accuracy.