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3D Brain and Heart Volume Generative Models: A Survey

2022-10-12 06:35:04
Yanbin Liu, Girish Dwivedi, Farid Boussaid, Mohammed Bennamoun
arXiv_CV
                       
Abstract

Generative models such as generative adversarial networks and autoencoders have gained a great deal of attention in the medical field due to their excellent data generation capability. This paper provides a comprehensive survey of generative models for three-dimensional (3D) volumes, focusing on the brain and heart. A new and elaborate taxonomy of unconditional and conditional generative models is proposed to cover diverse medical tasks for the brain and heart: unconditional synthesis, classification, conditional synthesis, segmentation, denoising, detection, and registration. We provide relevant background, examine each task and also suggest potential future directions. A list of the latest publications will be updated on Github to keep up with the rapid influx of papers at \url{this https URL}.

Abstract (translated)

URL

https://arxiv.org/abs/2210.05952

PDF

https://arxiv.org/pdf/2210.05952.pdf

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