计算机科学
概率逻辑
人工智能
编码(集合论)
磁共振弥散成像
过程(计算)
集合(抽象数据类型)
质量(理念)
相互依存
生成模型
机器学习
生成语法
磁共振成像
程序设计语言
哲学
放射科
认识论
法学
医学
政治学
作者
Wei Peng,Ehsan Adeli,Tomas M. Bosschieter,Sang Hyun Park,Qingyu Zhao,Kilian M. Pohl
标识
DOI:10.1007/978-3-031-43993-3_2
摘要
As acquiring MRIs is expensive, neuroscience studies struggle to attain a sufficient number of them for properly training deep learning models. This challenge could be reduced by MRI synthesis, for which Generative Adversarial Networks (GANs) are popular. GANs, however, are commonly unstable and struggle with creating diverse and high-quality data. A more stable alternative is Diffusion Probabilistic Models (DPMs) with a fine-grained training strategy. To overcome their need for extensive computational resources, we propose a conditional DPM (cDPM) with a memory-efficient process that generates realistic-looking brain MRIs. To this end, we train a 2D cDPM to generate an MRI subvolume conditioned on another subset of slices from the same MRI. By generating slices using arbitrary combinations between condition and target slices, the model only requires limited computational resources to learn interdependencies between slices even if they are spatially far apart. After having learned these dependencies via an attention network, a new anatomy-consistent 3D brain MRI is generated by repeatedly applying the cDPM. Our experiments demonstrate that our method can generate high-quality 3D MRIs that share a similar distribution to real MRIs while still diversifying the training set. The code is available at https://github.com/xiaoiker/mask3DMRI_diffusion and also will be released as part of MONAI, at https://github.com/Project-MONAI/GenerativeModels .
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