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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2210.05952 |
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| _version_ | 1866914868013039616 |
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| author | Liu, Yanbin Dwivedi, Girish Boussaid, Farid Bennamoun, Mohammed |
| author_facet | Liu, Yanbin Dwivedi, Girish Boussaid, Farid Bennamoun, Mohammed |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_05952 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | 3D Brain and Heart Volume Generative Models: A Survey Liu, Yanbin Dwivedi, Girish Boussaid, Farid Bennamoun, Mohammed Image and Video Processing Computer Vision and Pattern Recognition 92C55 (Primary), 68U10 (Secondary) I.4; J.3 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. |
| title | 3D Brain and Heart Volume Generative Models: A Survey |
| topic | Image and Video Processing Computer Vision and Pattern Recognition 92C55 (Primary), 68U10 (Secondary) I.4; J.3 |
| url | https://arxiv.org/abs/2210.05952 |