Recent deep learning models have attracted substantial attention in infant
brain analysis. These models have performed state-of-the-art performance, such
as semi-supervised techniques (e.g., Temporal Ensembling, mean teacher).
However, these models depend on an encoder-decoder structure with stacked local
operators to gather long-range information, and the local operators limit the
efficiency and effectiveness. Besides, the $MRI$ data contain different tissue
properties ($TPs$) such as $T1$ and $T2$. One major limitation of these models
is that they use both data as inputs to the segment process, i.e., the models
are trained on the dataset once, and it requires much computational and memory
requirements during inference. In this work, we address the above limitations
by designing a new deep-learning model, called 3D-DenseUNet, which works as
adaptable global aggregation blocks in down-sampling to solve the issue of
spatial information loss. The self-attention module connects the down-sampling
blocks to up-sampling blocks, and integrates the feature maps in three
dimensions of spatial and channel, effectively improving the representation
potential and discriminating ability of the model. Additionally, we propose a
new method called Two Independent Teachers ($2IT$), that summarizes the model
weights instead of label predictions. Each teacher model is trained on
different types of brain data, $T1$ and $T2$, respectively. Then, a fuse model
is added to improve test accuracy and enable training with fewer parameters and
labels compared to the Temporal Ensembling method without modifying the network
architecture. Empirical results demonstrate the effectiveness of the proposed
method.

本文提出了一个新的深度学习模型 3D-DenseUNet 以及 Two Independent Teachers 方法，用于提高婴儿脑数据分割的准确度和效率。

两位独立的教师是更好的榜样

Two Independent Teachers are Better Role Model

Graphs are widely used as a natural framework that captures interactions
between individual elements represented as nodes in a graph. In medical
applications, specifically, nodes can represent individuals within a
potentially large population (patients or healthy controls) accompanied by a
set of features, while the graph edges incorporate associations between
subjects in an intuitive manner. This representation allows to incorporate the
wealth of imaging and non-imaging information as well as individual subject
features simultaneously in disease classification tasks. Previous graph-based
approaches for supervised or unsupervised learning in the context of disease
prediction solely focus on pairwise similarities between subjects, disregarding
individual characteristics and features, or rather rely on subject-specific
imaging feature vectors and fail to model interactions between them. In this
paper, we present a thorough evaluation of a generic framework that leverages
both imaging and non-imaging information and can be used for brain analysis in
large populations. This framework exploits Graph Convolutional Networks (GCNs)
and involves representing populations as a sparse graph, where its nodes are
associated with imaging-based feature vectors, while phenotypic information is
integrated as edge weights. The extensive evaluation explores the effect of
each individual component of this framework on disease prediction performance
and further compares it to different baselines. The framework performance is
tested on two large datasets with diverse underlying data, ABIDE and ADNI, for
the prediction of Autism Spectrum Disorder and conversion to Alzheimer's
disease, respectively. Our analysis shows that our novel framework can improve
over state-of-the-art results on both databases, with 70.4% classification
accuracy for ABIDE and 80.0% for ADNI.

本文提出了一种利用图卷积网络的通用框架，该框架旨在将人群表示为一个稀疏图，其中其节点与基于成像的特征向量相关联，而表型信息则作为边权集成，以在大型人群中进行脑分析，本框架可以用于疾病预测任务，能够显著提高预测性能，并在测试了两个大型数据集，ABIDE 和 ADNI，以分别预测自闭症谱系障碍和转化为阿尔茨海默病的结果。

应用图卷积网络进行疾病预测：自闭症谱系障碍和阿尔茨海默病为例

Disease Prediction using Graph Convolutional Networks: Application to  Autism Spectrum Disorder and Alzheimer's Disease

Exploiting the wealth of imaging and non-imaging information for disease
prediction tasks requires models capable of representing, at the same time,
individual features as well as data associations between subjects from
potentially large populations. Graphs provide a natural framework for such
tasks, yet previous graph-based approaches focus on pairwise similarities
without modelling the subjects' individual characteristics and features. On the
other hand, relying solely on subject-specific imaging feature vectors fails to
model the interaction and similarity between subjects, which can reduce
performance. In this paper, we introduce the novel concept of Graph
Convolutional Networks (GCN) for brain analysis in populations, combining
imaging and non-imaging data. We represent populations as a sparse graph where
its vertices are associated with image-based feature vectors and the edges
encode phenotypic information. This structure was used to train a GCN model on
partially labelled graphs, aiming to infer the classes of unlabelled nodes from
the node features and pairwise associations between subjects. We demonstrate
the potential of the method on the challenging ADNI and ABIDE databases, as a
proof of concept of the benefit from integrating contextual information in
classification tasks. This has a clear impact on the quality of the
predictions, leading to 69.5% accuracy for ABIDE (outperforming the current
state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion,
significantly outperforming standard linear classifiers where only individual
features are considered.

本文介绍了在人群的脑分析中，将图卷积网络（GCN）应用于包括成像和非成像数据的稀疏图中，以推断未标记节点的类别，并在分类任务中整合上下文信息的潜力。该方法在 ABIDE 和 ADNI 数据库上取得了良好的性能。