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2018 | OriginalPaper | Buchkapitel

FDR-HS: An Empirical Bayesian Identification of Heterogenous Features in Neuroimage Analysis

verfasst von : Xinwei Sun, Lingjing Hu, Fandong Zhang, Yuan Yao, Yizhou Wang

Erschienen in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

Verlag: Springer International Publishing

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Abstract

Recent studies found that in voxel-based neuroimage analysis, detecting and differentiating “procedural bias” that are introduced during the preprocessing steps from lesion features, not only can help boost accuracy but also can improve interpretability. To the best of our knowledge, GSplit LBI is the first model proposed in the literature to simultaneously capture both procedural bias and lesion features. Despite the fact that it can improve prediction power by leveraging the procedural bias, it may select spurious features due to the multicollinearity in high dimensional space. Moreover, it does not take into account the heterogeneity of these two types of features. In fact, the procedural bias and lesion features differ in terms of volumetric change and spatial correlation pattern. To address these issues, we propose a “two-groups” Empirical-Bayes method called “FDR-HS” (False-Discovery-Rate Heterogenous Smoothing). Such method is able to not only avoid multicollinearity, but also exploit the heterogenous spatial patterns of features. In addition, it enjoys the simplicity in implementation by introducing hidden variables, which turns the problem into a convex optimization scheme and can be solved efficiently by the expectation-maximum (EM) algorithm. Empirical experiments have been evaluated on the Alzheimer’s Disease Neuroimage Initiative (ADNI) database. The advantage of the proposed model is verified by improved interpretability and prediction power using selected features by FDR-HS.

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Fußnoten
1
Please refer supplementary material for detailed and theoretical discussion.
 
2
Here \(\varvec{G} = (\varvec{V},\varvec{E})\), where \(\varvec{V}\) is the node set of voxels, \(\varvec{E}\) is the edge set of voxel pairs in neighbor (e.g. 3-by-3-by-3).
 
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Metadaten
Titel
FDR-HS: An Empirical Bayesian Identification of Heterogenous Features in Neuroimage Analysis
verfasst von
Xinwei Sun
Lingjing Hu
Fandong Zhang
Yuan Yao
Yizhou Wang
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-030-00928-1_69