论文标题
患者脑MRI的临床上合理的病理 - 解剖学分解，结构化变分先验
Clinically Plausible Pathology-Anatomy Disentanglement in Patient Brain MRI with Structured Variational Priors
论文作者
论文摘要
我们提出了一个层次结构化的变异推理模型，以准确地从受试者MRIS中的受试者特异性解剖结构中准确解散可观察到的疾病（例如脑病变或萎缩）的证据。我们的模型（1）借助灵活的，部分自回旋的先验，解决了MRI的解剖学和病理生成因子之间通常存在的微妙和细粒度依赖性，以确保生成样品的临床有效性； （2）保存和解剖与患者疾病状态有关的病理细节更细细。此外，我们尝试了一种替代培训配置，在该配置中，我们为潜在单元的子集提供监督。结果表明，（1）部分监督的潜在空间在疾病证据和特定于特定的解剖学之间达到了更高程度的分离； （2）当先验用自回归结构配制时，监督的知识可以传播到无监督的潜在单位，从而产生更有信息的潜在表示，能够建模解剖学 - 病理学的相互依存关系。
We propose a hierarchically structured variational inference model for accurately disentangling observable evidence of disease (e.g. brain lesions or atrophy) from subject-specific anatomy in brain MRIs. With flexible, partially autoregressive priors, our model (1) addresses the subtle and fine-grained dependencies that typically exist between anatomical and pathological generating factors of an MRI to ensure the clinical validity of generated samples; (2) preserves and disentangles finer pathological details pertaining to a patient's disease state. Additionally, we experiment with an alternative training configuration where we provide supervision to a subset of latent units. It is shown that (1) a partially supervised latent space achieves a higher degree of disentanglement between evidence of disease and subject-specific anatomy; (2) when the prior is formulated with an autoregressive structure, knowledge from the supervision can propagate to the unsupervised latent units, resulting in more informative latent representations capable of modelling anatomy-pathology interdependencies.
