Hetero-multi-modal stroke diagnosis and prognosis deep neural network model

在这项工作中，我们正在处理创建一个特定于中风的诊断和预后模型，该模型是多模态和异模态的，其核心是一个具有高度形态描述性潜在表示的生成模型。潜在表示将用作成像模态的压缩表示，并与从嵌入神经学报告中提取的潜在特征相结合，目的是诊断和预后谵妄。最后，我们添加了某种形式的内省，以突出每个模态的哪些部分对手头的任务最重要。

In this piece of work, we are tackling the creating of a diagnosis and prognosis model specific for stroke that is multi-modal and hetero-modal with the core of the model is a generative one that has a highly morphologically descriptive latent representation. The latent representation will be used as a compressed representation of the imaging modalities and be combined with latent features extracted from embedding the neurological reports with the aim of diagnosis and prognosis for delirium. Lastly, we added some form of introspection to highlight what parts of each modality are the most important for the task at hand.