Frontotemporal Dementia (FTD) diagnosis has been successfully progress using
deep learning techniques. However, current FTD identification methods suffer
from two limitations. Firstly, they do not exploit the potential of multi-view
functional magnetic resonance imaging (fMRI) for classifying FTD. Secondly,
they do not consider the reliability of the multi-view FTD diagnosis. To
address these limitations, we propose a reliable multi-view impartial decision
network (MID-Net) for FTD diagnosis in fMRI. Our MID-Net provides confidence
for each view and generates a reliable prediction without any conflict. To
achieve this, we employ multiple expert models to extract evidence from the
abundant neural network information contained in fMRI images. We then introduce
the Dirichlet Distribution to characterize the expert class probability
distribution from an evidence level. Additionally, a novel Impartial Decision
Maker (IDer) is proposed to combine the different opinions inductively to
arrive at an unbiased prediction without additional computation cost. Overall,
our MID-Net dynamically integrates the decisions of different experts on FTD
disease, especially when dealing with multi-view high-conflict cases. Extensive
experiments on a high-quality FTD fMRI dataset demonstrate that our model
outperforms previous methods and provides high uncertainty for hard-to-classify
examples. We believe that our approach represents a significant step toward the
deployment of reliable FTD decision-making under multi-expert conditions. We
will release the codes for reproduction after acceptance.

我们提出了一种可靠的多视角公正决策网络 (MID-Net)，用于 fMRI 中的 FTD 诊断。我们的 MID-Net 从 fMRI 图像中提取信息，利用多个专家模型来进行证据提取，并引入狄利克雷分布来表征每个专家的类概率分布。我们还提出了一种新颖的公正决策制定者 (IDer)，以在感性地结合不同意见的同时，不增加计算成本地得出一个无偏预测。我们的实验结果表明，我们的模型优于之前的方法，并为难以分类的例子提供高不确定性。

多视角公正决策网络用于额颞型痴呆症诊断

A Multi-view Impartial Decision Network for Frontotemporal Dementia  Diagnosis

This paper introduces an innovative physics-informed deep learning framework
for metamodeling of nonlinear structural systems with scarce data. The basic
concept is to incorporate physics knowledge (e.g., laws of physics, scientific
principles) into deep long short-term memory (LSTM) networks, which boosts the
learning within a feasible solution space. The physics constraints are embedded
in the loss function to enforce the model training which can accurately capture
latent system nonlinearity even with very limited available training datasets.
Specifically for dynamic structures, physical laws of equation of motion, state
dependency and hysteretic constitutive relationship are considered to construct
the physics loss. In particular, two physics-informed multi-LSTM network
architectures are proposed for structural metamodeling. The satisfactory
performance of the proposed framework is successfully demonstrated through two
illustrative examples (e.g., nonlinear structures subjected to ground motion
excitation). It turns out that the embedded physics can alleviate overfitting
issues, reduce the need of big training datasets, and improve the robustness of
the trained model for more reliable prediction. As a result, the
physics-informed deep learning paradigm outperforms classical
non-physics-guided data-driven neural networks.

本文介绍了一种基于物理知识的深度学习框架，其利用少量数据对非线性结构系统进行元建模，并成功演示了其对复杂结构的精确预测性能。