计算机科学
人工智能
翻译(生物学)
图像(数学)
分割
任务(项目管理)
过程(计算)
计算机视觉
航程(航空)
噪音(视频)
降噪
图像分割
扩散
图像翻译
磁共振弥散成像
模式识别(心理学)
磁共振成像
工程类
医学
生物化学
化学
物理
热力学
系统工程
放射科
信使核糖核酸
基因
航空航天工程
操作系统
作者
Julia Wolleb,Robin Sandkühler,Florentin Bieder,Philippe C. Cattin
出处
期刊:Cornell University - arXiv
日期:2022-01-01
被引量:1
标识
DOI:10.48550/arxiv.2204.02641
摘要
Recently, diffusion models were applied to a wide range of image analysis tasks. We build on a method for image-to-image translation using denoising diffusion implicit models and include a regression problem and a segmentation problem for guiding the image generation to the desired output. The main advantage of our approach is that the guidance during the denoising process is done by an external gradient. Consequently, the diffusion model does not need to be retrained for the different tasks on the same dataset. We apply our method to simulate the aging process on facial photos using a regression task, as well as on a brain magnetic resonance (MR) imaging dataset for the simulation of brain tumor growth. Furthermore, we use a segmentation model to inpaint tumors at the desired location in healthy slices of brain MR images. We achieve convincing results for all problems.
科研通智能强力驱动
Strongly Powered by AbleSci AI