神经胶质瘤生长的深度概率模型
Deep Probabilistic Modeling of Glioma Growth
摘要：
现有的方法来模拟脑肿瘤生长的动力学，特别是神经胶质瘤，采用生物启发的细胞扩散模型，使用图像数据来估计相关的参数。在这项工作中，我们提出了一种基于概率分割和表示学习的最新进展的替代方法，该方法直接从数据中隐式学习增长动态，而无需底层显式模型。我们提出的证据表明，我们的方法是能够学习一个合理的未来肿瘤外观的分布条件下，过去的观察相同的肿瘤。
英文摘要：
Existing approaches to modeling the dynamics of brain tumor growth, specifically glioma, employ biologically inspired models of cell diffusion, using image data to estimate the associated parameters. In this work, we propose an alternative approach based on recent advances in probabilistic segmentation and representation learning that implicitly learns growth dynamics directly from data without an underlying explicit model. We present evidence that our approach is able to learn a distribution of plausible future tumor appearances conditioned on past observations of the same tumor.
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