Brain age estimation is clinically important as it can provide valuable
information in the context of neurodegenerative diseases such as Alzheimer's.
Population graphs, which include multimodal imaging information of the subjects
along with the relationships among the population, have been used in literature
along with Graph Convolutional Networks (GCNs) and have proved beneficial for a
variety of medical imaging tasks. A population graph is usually static and
constructed manually using non-imaging information. However, graph construction
is not a trivial task and might significantly affect the performance of the
GCN, which is inherently very sensitive to the graph structure. In this work,
we propose a framework that learns a population graph structure optimized for
the downstream task. An attention mechanism assigns weights to a set of imaging
and non-imaging features (phenotypes), which are then used for edge extraction.
The resulting graph is used to train the GCN. The entire pipeline can be
trained end-to-end. Additionally, by visualizing the attention weights that
were the most important for the graph construction, we increase the
interpretability of the graph. We use the UK Biobank, which provides a large
variety of neuroimaging and non-imaging phenotypes, to evaluate our method on
brain age regression and classification. The proposed method outperforms
competing static graph approaches and other state-of-the-art adaptive methods.
We further show that the assigned attention scores indicate that there are both
imaging and non-imaging phenotypes that are informative for brain age
estimation and are in agreement with the relevant literature.

提出一种优化下游任务的人口图结构学习框架，通过注意机制为一组成像和非成像特征分配权重，用于边提取，并使用生成的图来训练图卷积网络，结果表明该方法在脑龄回归和分类方面优于固定图方法和其他最先进的自适应方法，并且赋予的注意程度指示了成像和非成像特征对脑龄估计的信息价值与相关文献一致。

可解释的自适应人群图学习的多模态脑龄估计

Multimodal brain age estimation using interpretable adaptive  population-graph learning

Recently, Graph Convolutional Networks (GCNs) have proven to be a powerful
machine learning tool for Computer-Aided Diagnosis (CADx) and disease
prediction. A key component in these models is to build a population graph,
where the graph adjacency matrix represents pair-wise patient similarities.
Until now, the similarity metrics have been defined manually, usually based on
meta-features like demographics or clinical scores. The definition of the
metric, however, needs careful tuning, as GCNs are very sensitive to the graph
structure. In this paper, we demonstrate for the first time in the CADx domain
that it is possible to learn a single, optimal graph towards the GCN's
downstream task of disease classification. To this end, we propose a novel,
end-to-end trainable graph learning architecture for dynamic and localized
graph pruning. Unlike commonly employed spectral GCN approaches, our GCN is
spatial and inductive, and can thus infer previously unseen patients as well.
We demonstrate significant classification improvements with our learned graph
on two CADx problems in medicine. We further explain and visualize this result
using an artificial dataset, underlining the importance of graph learning for
more accurate and robust inference with GCNs in medical applications.

本文通过提出新的动态本地化图剪枝方法，在医学中的两个 CADx 问题上展示了学习单一、最优图形对 GCN 向下游疾病分类任务的可行性，由此我们证明了图形学习对于在医学应用中使用 GCN 进行更准确和稳健推断的重要性。