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

Neural network-based anomaly detection remains challenging in clinical
applications with little or no supervised information and subtle anomalies such
as hardly visible brain lesions. Among unsupervised methods, patch-based
auto-encoders with their efficient representation power provided by their
latent space, have shown good results for visible lesion detection. However,
the commonly used reconstruction error criterion may limit their performance
when facing less obvious lesions. In this work, we design two alternative
detection criteria. They are derived from multivariate analysis and can more
directly capture information from latent space representations. Their
performance compares favorably with two additional supervised learning methods,
on a difficult de novo Parkinson Disease (PD) classification task.

利用无监督学习中的基于 Patch 的自编码器，设计出两个用于检测潜在异常信息的检测标准，与两个监督式学习的方法相比较，这两个标准在难度较大的 PD 分类任务上的表现更佳。