Multi-Channel Stochastic Variational Inference for the Joint Analysis of Heterogeneous Biomedical Data in Alzheimer's Disease - Archive ouverte HAL Access content directly
Conference Papers Year : 2018

Multi-Channel Stochastic Variational Inference for the Joint Analysis of Heterogeneous Biomedical Data in Alzheimer's Disease

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Abstract

The joint analysis of biomedical data in Alzheimer's Disease (AD) is important for better clinical diagnosis and to understand the relationship between biomarkers. However, jointly accounting for heterogeneous measures poses important challenges related to the modeling of heterogeneity and to the interpretability of the results. These issues are here addressed by proposing a novel multi-channel stochastic generative model. We assume that a latent variable generates the data observed through different channels (e.g., clinical scores, imaging) and we describe an efficient way to estimate jointly the distribution of the latent variable and the data generative process. Experiments on synthetic data show that the multi-channel formulation allows superior data reconstruction as opposed to the single channel one. Moreover, the derived lower bound of the model evidence represents a promising model selection criterion. Experiments on AD data show that the model parameters can be used for unsupervised patient stratification and for the joint interpretation of the heterogeneous observations. Because of its general and flexible formulation , we believe that the proposed method can find various applications as a general data fusion technique.
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Dates and versions

hal-02397737 , version 1 (27-09-2018)
hal-02397737 , version 2 (06-12-2019)

Identifiers

  • HAL Id : hal-02397737 , version 2

Cite

Luigi Antelmi, Nicholas Ayache, Philippe Robert, Marco Lorenzi. Multi-Channel Stochastic Variational Inference for the Joint Analysis of Heterogeneous Biomedical Data in Alzheimer's Disease. Understanding and Interpreting Machine Learning in Medical Image Computing Applications, Sep 2018, Granada, Spain. pp.15-23. ⟨hal-02397737v2⟩
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