Parkinson disease is the second most common neurodegenerative disorder, as
reported by the World Health Organization. In this paper, we propose a direct
three-Class PD classification using two different modalities, namely, MRI and
DTI. The three classes used for classification are PD, Scans Without Evidence
of Dopamine Deficit and Healthy Control. We use white matter and gray matter
from the MRI and fractional anisotropy and mean diffusivity from the DTI to
achieve our goal. We train four separate CNNs on the above four types of data.
At the decision level, the outputs of the four CNN models are fused with an
optimal weighted average fusion technique. We achieve an accuracy of 95.53
percentage for the direct three class classification of PD, HC and SWEDD on the
publicly available PPMI database. Extensive comparisons including a series of
ablation studies clearly demonstrate the effectiveness of our proposed
solution.

本文提出了使用 MRI 和 DTI 两种不同模态的直接三类 PD 分类方法，通过使用白质和灰质 MRI 数据以及 DTI 数据的有限阻抗和平均扩散度，利用四个独立 CNN 模型进行决策层融合，实现了对公开 PPMI 数据库中 PD、健康人群和无多巴胺缺陷证据扫描的直接三类 PD 分类，获得了 95.53% 的准确率。通过一系列消融研究明确证明了我们提出的解决方案的有效性。

多模态多类帕金森病分类的 CNN 和决策级融合

Multi-modal multi-class Parkinson disease classification using CNN and  decision level fusion

Integrating the brain structural and functional connectivity features is of
great significance in both exploring brain science and analyzing cognitive
impairment clinically. However, it remains a challenge to effectively fuse
structural and functional features in exploring the brain network. In this
paper, a novel brain structure-function fusing-representation learning (BSFL)
model is proposed to effectively learn fused representation from diffusion
tensor imaging (DTI) and resting-state functional magnetic resonance imaging
(fMRI) for mild cognitive impairment (MCI) analysis. Specifically, the
decomposition-fusion framework is developed to first decompose the feature
space into the union of the uniform and the unique spaces for each modality,
and then adaptively fuse the decomposed features to learn MCI-related
representation. Moreover, a knowledge-aware transformer module is designed to
automatically capture local and global connectivity features throughout the
brain. Also, a uniform-unique contrastive loss is further devised to make the
decomposition more effective and enhance the complementarity of structural and
functional features. The extensive experiments demonstrate that the proposed
model achieves better performance than other competitive methods in predicting
and analyzing MCI. More importantly, the proposed model could be a potential
tool for reconstructing unified brain networks and predicting abnormal
connections during the degenerative processes in MCI.

本研究提出一种新的脑结构 - 功能融合表征学习模型，它可用于从 DTI 和 fMRI 中学习融合表示，以分析轻度认知障碍 (MCI)。该模型通过将特征空间分解为每种模态的统一和独特空间的并集，并自适应融合分解功能，来有效融合脑的结构和功能特征，同时设计了一种知识感知变压器模块来捕获全局和局部连接特征。实验表明，该模型在预测和分析 MCI 方面的表现优于其他竞争方法，有望成为重建统一脑网络并预测 MCI 退化的异常连接的潜在工具。