Paper Info
Title
Pattern Visualization and Recognition Using Tensor Factorization for Early Differential Diagnosis of Parkinsonism.
Abstract
Idiopathic Parkinsons disease (PD) and atypical parkinsonian syndromes may have similar symptoms at the early disease stage. Pattern recognition on metabolic imaging has been confirmed of distinct value in the early differential diagnosis of Parkinsonism. However, the principal component analysis (PCA) based method ends up with a unique probability score of each disease pattern. This restricts the exploration of heterogeneous characteristic features for differentiation. There is no visualization of the underlying mechanism to assist the radiologist/neurologist either. We propose a tensor factorization based method to extract the characteristic patterns of the diseases. By decomposing the 3D data, we can capture the intrinsic characteristic pattern in the data. In particular, the disease-related patterns can be visualized individually for the inspection by physicians. The test on PET images of 206 early parkinsonian patients has confirmed differential patterns on the visualized feature images using the proposed method. Computer-aided diagnosis based on multi-class support vector machine (SVM) shown improved diagnostic accuracy of Parkinsonism using the tensor-factorized feature images compared to the state-of-the-art PCA-based scores [Tang et al. Lancet Neurol. 2010].
Year
Venue
Field
2017
MICCAI
Computer vision,Pattern recognition,Visualization,Computer science,Support vector machine,Parkinsonism,Parkinsonian syndromes,Artificial intelligence,Tensor factorization,Principal component analysis,Differential diagnosis
DocType
Citations 
PageRank 
Conference
1
0.36
References 
Authors
11
11
Name
Order
Citations
PageRank
Rui Li120.71
Ping Wu221.18
Igor Yakushev3103.09
Wang, J.441.08
Sibylle Ziegler575.80
Stefan Förster610.36
Sung-Cheng Huang711.04
Markus Schwaiger820.85
Nassir Navab96594578.60
Chuantao Zuo1010.70
Kuangyu Shi11368.12