连续时间深部胶质瘤生长模型
Continuous-Time Deep Glioma Growth Models
摘要：
估计肿瘤未来可能如何演变的能力可能具有巨大的临床益处，从改善治疗决策到更好的放射治疗剂量分布。最近的工作通过深度学习和变分推理来解决胶质瘤生长建模问题，从而完全从真实的患者数据分布中学习生长动态。到目前为止，这种方法被限制到预定义的图像采集间隔和固定长度的序列，这限制了它在更现实的场景中的适用性。我们通过扩展神经过程克服了这些限制，神经过程是随机时间序列的一类条件生成模型，具有分层多尺度表示编码，包括时空注意机制。其结果是一个学习的增长模型，它可以以任意数量的观察为条件，并且可以在连续的时间轴上产生时间上一致的增长轨迹的分布。在379名患者的数据集上，该方法成功地捕获了图像中的全局和细粒度变化，与其他学习的生长模型相比，表现出优越的性能。
英文摘要：
The ability to estimate how a tumor might evolve in the future could have tremendous clinical benefits, from improved treatment decisions to better dose distribution in radiation therapy. Recent work has approached the glioma growth modeling problem via deep learning and variational inference, thus learning growth dynamics entirely from a real patient data distribution. So far, this approach was constrained to predefined image acquisition intervals and sequences of fixed length, which limits its applicability in more realistic scenarios. We overcome these limitations by extending Neural Processes, a class of conditional generative models for stochastic time series, with a hierarchical multi-scale representation encoding including a spatio-temporal attention mechanism. The result is a learned growth model that can be conditioned on an arbitrary number of observations, and that can produce a distribution of temporally consistent growth trajectories on a continuous time axis. On a dataset of 379 patients, the approach successfully captures both global and finer-grained variations in the images, exhibiting superior performance compared to other learned growth models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
