反向时空疾病进展建模
Reverse Spatio-Temporal Disease Progression Modelling
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中文总结 AI 辅助
针对现有疾病进展模型忽视潜伏期且仅正向预测的问题，提出反向时空疾病进展建模，利用向量量化自编码器和神经常微分方程从后期患病影像重建早期健康状态，在基准和真实MRI数据上优于基线。
中文摘要 AI 辅助
基于深度学习的时空疾病进展模型通常忽视进行性疾病（如阿尔茨海默病）的潜伏期，限制了这些模型在早期干预中的应用，而早期干预对于这类不易逆转的疾病至关重要。这是因为现有的基于深度学习的纵向疾病进展模型几乎总是正向运行的：从观察到的基线预测未来的衰退。然而，在许多临床场景中，影像学检查仅在病理被怀疑或已经可见后才开始，更早的、更健康的患者特异性参考图像从未被采集。为解决这一问题，我们提出研究反向疾病进展预测：给定后期患病的解剖结构，重建其之前未被观察到的更健康解剖结构。我们采用两阶段模型，其中冻结的3D向量量化自编码器定义了一个紧凑的离散潜在空间，而神经常微分方程（ODE）在该空间中学习连续时间动态。一个循环编码器按反向时间顺序读取后期观测，初始化潜在状态，然后ODE沿轨迹向后积分。在带有正弦扰动的受控Morpho-MNIST基准上，我们的模型成功地从非单调轨迹的后期观测中恢复了未见过的先前状态。在来自阿尔茨海默病神经影像学倡议的纵向脑部MRI上，在从观察到的后期患病状态恢复患者先前未见的健康轨迹的任务中，我们的模型优于使用最近复制和均值观测的基线，在每个诊断分层中均获得正向疾病逆转分数。我们希望我们的工作能为从单次扫描中发现潜伏期以及基于影像开发疾病的早期干预提供见解和工具。
英文摘要
Deep learning-based spatio-temporal disease progression models commonly overlook the incubation period of progressive diseases, limiting the use of those models in early interventions, which are vital for not easily reversible diseases such as Alzheimer's. This is because, the existing deep learning based longitudinal disease-progression models are almost always run forward: from an observed baseline they predict future decline. In many clinical settings, however, imaging begins only after pathology is suspected or already visible, the earlier, healthier patient-specific reference was never acquired. To address this, we propose to study reverse disease progression prediction: given later diseased anatomy, reconstruct the unobserved healthier anatomy that preceded it. We use a two-stage model in which a frozen 3D vector-quantised autoencoder defines a compact discrete latent space, while a Neural Ordinary Differential Equation (ODE) learns continuous-time dynamics in that space. A recurrent encoder reads late observations in reverse temporal order, initialises the latent state, and the ODE is integrated backwards across the trajectory. On a controlled Morpho-MNIST benchmark with a sinusoidal perturbation, our model successfully recovered the unseen previous states from later observations of the non-monotonic trajectory. On longitudinal brain MRIs from Alzheimer's Disease Neuroimaging Initiative, at the task to recover the previous unseen trajectory towards healthy states of the patients from observed later diseased states, our model outperforms the baselines that uses copy-nearest and mean-observed, with positive disease-reversal scores in every diagnostic stratum. We hope that our work can provide insights and tools towards discovering the incubation periods from single-shot scans, and developing early interventions of diseases based on imaging.
发表机构
- Télécom SudParis, Institut Polytechnique de Paris（巴黎电信 SudParis，巴黎综合理工学院）
- University College London（伦敦大学学院）
- Digital Technologies and Artificial Intelligence Development Research Institute (AIRI)（数字技术与人工智能发展研究院（AIRI））
机构由 AI 辅助整理，请以论文原文为准。
