A novel semi-supervised multi-view clustering framework for screening Parkinson's disease.

Chunlin Wang,Yiling Zhang,Donghai Zhai,Xiaobo Zhang,Yan Yang

doi:10.3934/mbe.2020192

Chunlin Wang, Yiling Zhang + Show 3 more

Open Access

https://doi.org/10.3934/mbe.2020192

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Abstract

In recent years, there are many research cases for the diagnosis of Parkinson's disease (PD) with the brain magnetic resonance imaging (MRI) by utilizing the traditional unsupervised machine learning methods and the supervised deep learning models. However, unsupervised learning methods are not good at extracting accurate features among MRIs and it is difficult to collect enough data in the field of PD to satisfy the need of training deep learning models. Moreover, most of the existing studies are based on single-view MRI data, of which data characteristics are not sufficient enough. In this paper, therefore, in order to tackle the drawbacks mentioned above, we propose a novel semi-supervised learning framework called Semi-supervised Multi-view learning Clustering architecture technology (SMC). The model firstly introduces the sliding window method to grasp different features, and then uses the dimensionality reduction algorithms of Linear Discriminant Analysis (LDA) to process the data with different features. Finally, the traditional single-view clustering and multi-view clustering methods are employed on multiple feature views to obtain the results. Experiments show that our proposed method is superior to the state-of-art unsupervised learning models on the clustering effect. As a result, it may be noted that, our work could contribute to improving the effectiveness of identifying PD by previous labeled and subsequent unlabeled medical MRI data in the realistic medical environment.

Highlights

Parkinson's disease (PD) is a degenerative and disabling disease in the nervous system [1], which generally occurs in the elderly
We demonstrate the effectiveness of supervised Multi-view learning Clustering architecture technology (SMC) for screening PD
To verify the effectiveness of semi-supervised technique, we evaluate the clustering results using SMC with those with traditional Unsupervised Clustering by principal component analysis (PCA) (UCP) on 6 single-view clustering methods, including Gaussian Mixture Model (GMM), K-Means, K-Medoids, AC, Balanced Iterative Reducing and Clustering Using Hierarchies (Birch), Spectral Clustering algorithm (SC) and 1 multiview clustering algorithm, namely Robust Multi-View K-Means Clustering (RMKMC)

Summary

Introduction

Parkinson's disease (PD) is a degenerative and disabling disease in the nervous system [1], which generally occurs in the elderly. Its clinical manifestations mainly include quiescent tremor, motor retardation, myotonia and postural gait disorder. For the elderly, screening PD as early as possible is very vital for prevention and delaying progress to assist in auxiliary diagnosis. The diagnosis of PD mainly relies on the clinical symptoms of patients and the professional knowledge of clinical neurologists. Most doctors would recommend inspecting a neuroimaging examination before the formal clinical diagnosis of PD, containing magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and positron emission tomography (PET), etc. We evaluate the proposed method on the MRI data obtained from the Parkinson’s Progression Markers Initiative (PPMI) platform [7]

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