Deep generative models are increasingly useful in medical image analysis to solve various issues, including class imbalance in classification tasks, motivating the development of multiple methods, where the Variational Autoencoder (VAE) is recognized as one of the most popular image generators. However, the utilization of convolutional layers in VAEs weakens their ability to model global context and long-range dependencies. This paper presents CAT-VAE, a hybrid approach based on VAE and Cross-Attention Transformers (CAT), in which a cross-attention mechanism is employed to promote long-range dependencies and improve the quality of the generated images. On the Ultrasound breast cancer dataset, the CAT-VAE achieved better image quality (FID 8.7659 for Malignant and 7.8761 for Normal) compared to the standard VAE. An experiment was conducted where a CNN classifier model was trained without data augmentation, with augmentation based on VAE, and using synthetic data generated by CAT-VAE. The CNN achieved the highest accuracy (97.00%) when trained with CAT-VAE synthetic images. A classification accuracy of 86.67% was achieved with mixed datasets of real and synthetic images, demonstrating that CAT-VAE improves generalization and resilience. These results highlight CAT-VAE's ability to produce diverse and realistic synthetic datasets.

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.