VAE可生成逼真医学图像，助力数据增强与模型精度提升
Exploring Variational Autoencoders for Medical Image Generation: A Comprehensive Study
- 通过变分自编码架构生成合成医学图像
- 提升小样本及不平衡数据集的分割与分类准确率
- 对比GAN等模型，在图像质量与多样性上表现更优
变分自编码器（VAE）是医学图像生成领域的重要技术，近年来已发展出多种架构。本文系统综述了VAE在医学影像中的研究进展，重点关注其生成接近真实数据的合成图像以实现数据增强的能力。文章梳理了关键架构与方法，并与生成对抗网络（GAN）等生成模型在图像质量、生成样本多样性等方面进行比较。研究表明，VAE能有效改善小样本和类别不平衡数据集的性能，显著提升医学图像分割与分类的准确性，已在多个医学领域展现应用价值。
原文摘要 · Abstract (English)
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.
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