论文标题
3D大脑和心脏体积生成模型：调查
3D Brain and Heart Volume Generative Models: A Survey
论文作者
论文摘要
由于其出色的数据生成能力，诸如生成对抗网络和自动编码器之类的生成模型在医疗领域引起了很多关注。本文对三维（3D）体积的生成模型进行了全面的调查，重点是大脑和心脏。提出了一种无条件和条件生成模型的新的分类法，以涵盖大脑和心脏的多种医学任务：无条件的合成，分类，有条件的合成，分割，分割，denosising，deconising，tintection，tintection和coptration。我们提供相关的背景，检查每个任务，并提出潜在的未来方向。最新出版物的列表将在Github上更新，以跟上https://github.com/csyanbin/3d-medical-generative-survey的快速涌入。
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 https://github.com/csyanbin/3D-Medical-Generative-Survey.
