Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data

International audienceInterpretable modeling of heterogeneous data channels is essential in medical applications, for example when jointly analyzing clinical scores and medical images. Variational Autoencoders (VAE) are powerful generative models that learn representations of complex data. The flexibility of VAE may come at the expense of lack of interpretability in describing the joint relationship between heterogeneous data. To tackle this problem, in this work we extend the variational framework of VAE to bring parsimony and inter-pretability when jointly account for latent relationships across multiple channels. In the latent space, this is achieved by constraining the varia-tional distribution of each channel to a common target prior. Parsimonious latent representations are enforced by variational dropout. Experiments on synthetic data show that our model correctly identifies the prescribed latent dimensions and data relationships across multiple testing scenarios. When applied to imaging and clinical data, our method allows to identify the joint effect of age and pathology in describing clinical condition in a large scale clinical cohort

Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data

International audienceInterpretable modeling of heterogeneous data channels is essential in medical applications, for example when jointly analyzing clinical scores and medical images. Variational Autoencoders (VAE) are powerful generative models that learn representations of complex data. The flexibility of VAE may come at the expense of lack of interpretability in describing the joint relationship between heterogeneous data. To tackle this problem, in this work we extend the variational framework of VAE to bring parsimony and inter-pretability when jointly account for latent relationships across multiple channels. In the latent space, this is achieved by constraining the varia-tional distribution of each channel to a common target prior. Parsimonious latent representations are enforced by variational dropout. Experiments on synthetic data show that our model correctly identifies the prescribed latent dimensions and data relationships across multiple testing scenarios. When applied to imaging and clinical data, our method allows to identify the joint effect of age and pathology in describing clinical condition in a large scale clinical cohort

Simulating the outcome of amyloid treatments in Alzheimer's Disease from multi-modal imaging and clinical data

International audienceIn this study we investigate a novel quantitative instrument for the development of intervention strategies for disease modifying drugs in Alzheimer's disease. Our framework is based on the modeling of the spatio-temporal dynamics governing the joint evolution of imaging and clinical biomarkers along the history of the disease, and allows the simulation of the effect of intervention time and drug dosage on the biomarkers' progression. When applied to multi-modal imaging and clinical data from the Alzheimer's Disease Neuroimaging Initiative our method enables to generate hypothetical scenarios of amyloid lowering interventions. The results quantify the crucial role of intervention time, and provide a theoretical justification for testing amyloid modifying drugs in the pre-clinical stage. Our experimental simulations are compatible with the outcomes observed in past clinical trials, and suggest that anti-amyloid treatments should be administered at least 7 years earlier than what is currently being done in order to obtain statistically powered improvement of clinical endpoints

Combining Multi-Task Learning and Multi-Channel Variational Auto-Encoders to Exploit Datasets with Missing Observations -Application to Multi-Modal Neuroimaging Studies in Dementia

The joint modeling of neuroimaging data across multiple datasets requires to consistently analyze high-dimensional and heterogeneous information in presence of often non-overlapping sets of views across data samples (e.g. imaging data, clinical scores, biological measurements). This analysis is associated with the problem of missing information across datasets, which can happen in two forms: missing at random (MAR), when the absence of a view is unpredictable and does not depend on the dataset (e.g. due to data corruption); missing not at random (MNAR), when a specific view is absent by design for a specific dataset. In order to take advantage of the increased variability and sample size when pooling together observations from many cohorts and at the same time cope with the ubiquitous problem of missing information, we propose here a multi-task generative latent-variable model where the common variability across datasets stems from the estimation of a shared latent representation across views. Our formulation allows to retrieve a consistent latent representation common to all views and datasets, even in the presence of missing information. Simulations on synthetic data show that our method is able to identify a common latent representation of multi-view datasets, even when the compatibility across datasets is minimal. When jointly analyzing multi-modal neuroimaging and clinical data from real independent dementia studies, our model is able to mitigate the absence of modalities without having to discard any available information. Moreover, the common latent representation inferred with our model can be used to define robust classifiers gathering the combined information across different datasets. To conclude, both on synthetic and real data experiments, our model compared favorably to state of the art benchmark methods, providing a more powerful exploitation of multi-modal observations with missing views

Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data

International audienceInterpretable modeling of heterogeneous data channels is essential in medical applications, for example when jointly analyzing clinical scores and medical images. Variational Autoencoders (VAE) are powerful generative models that learn representations of complex data. The flexibility of VAE may come at the expense of lack of interpretability in describing the joint relationship between heterogeneous data. To tackle this problem, in this work we extend the variational framework of VAE to bring parsimony and inter-pretability when jointly account for latent relationships across multiple channels. In the latent space, this is achieved by constraining the varia-tional distribution of each channel to a common target prior. Parsimonious latent representations are enforced by variational dropout. Experiments on synthetic data show that our model correctly identifies the prescribed latent dimensions and data relationships across multiple testing scenarios. When applied to imaging and clinical data, our method allows to identify the joint effect of age and pathology in describing clinical condition in a large scale clinical cohort

Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data

International audienceInterpretable modeling of heterogeneous data channels is essential in medical applications, for example when jointly analyzing clinical scores and medical images. Variational Autoencoders (VAE) are powerful generative models that learn representations of complex data. The flexibility of VAE may come at the expense of lack of interpretability in describing the joint relationship between heterogeneous data. To tackle this problem, in this work we extend the variational framework of VAE to bring parsimony and inter-pretability when jointly account for latent relationships across multiple channels. In the latent space, this is achieved by constraining the varia-tional distribution of each channel to a common target prior. Parsimonious latent representations are enforced by variational dropout. Experiments on synthetic data show that our model correctly identifies the prescribed latent dimensions and data relationships across multiple testing scenarios. When applied to imaging and clinical data, our method allows to identify the joint effect of age and pathology in describing clinical condition in a large scale clinical cohort