Paper Info
Title
Multi-modal Disease Classification in Incomplete Datasets Using Geometric Matrix Completion.
Abstract
In large population-based studies and in clinical routine, tasks like disease diagnosis and progression prediction are inherently based on a rich set of multi-modal data, including imaging and other sensor data, clinical scores, phenotypes, labels and demographics. However, missing features, rater bias and inaccurate measurements are typical ailments of real-life medical datasets. Recently, it has been shown that deep learning with graph convolution neural networks (GCN) can outperform traditional machine learning in disease classification, but missing features remain an open problem. In this work, we follow up on the idea of modeling multi-modal disease classification as a matrix completion problem, with simultaneous classification and non-linear imputation of features. Compared to methods before, we arrange subjects in a graph-structure and solve classification through geometric matrix completion, which simulates a heat diffusion process that is learned and solved with a recurrent neural network. We demonstrate the potential of this method on the ADNI-based TADPOLE dataset and on the task of predicting the transition from MCI to Alzheimer’s disease. With an AUC of 0.950 and classification accuracy of 87%, our approach outperforms standard linear and non-linear classifiers, as well as several state-of-the-art results in related literature, including a recently proposed GCN-based approach.
Year
DOI
Venue
2018
10.1007/978-3-030-00689-1_3
GRAIL/Beyond-MIC@MICCAI
DocType
Volume
Citations 
Conference
abs/1803.11550
0
PageRank 
References 
Authors
0.34
11
4
Name
Order
Citations
PageRank
Gerome Vivar100.34
Andreas Zwergal201.69
Nassir Navab36594578.60
Seyed-Ahmad Ahmadi458630.84