Disease Progression Score Estimation From Multimodal Imaging and MicroRNA Data Using Supervised Variational Autoencoders

Author:

Kmetzsch Virgilio1ORCID,Becker Emmanuelle2,Saracino Dario1ORCID,Rinaldi Daisy3,Camuzat Agnes3,Le Ber Isabelle4,Colliot Olivier1ORCID

Affiliation:

1. Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Sorbonne Université, Paris, France

2. Inria, CNRS, IRISA, Univ Rennes, Rennes, France

3. Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inserm, AP-HP, Sorbonne Université, Paris, France

4. Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, Institute of Memory and Alzheimer's Disease (IM2A), Sorbonne Université, Paris, France

Funder

French government

Inria Project Lab Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Reference49 articles.

1. A multimodal variational autoencoder for estimating progression scores from imaging and microRNA data in rare neurodegenerative diseases

2. Multi-modal anomaly detection for unstructured and uncertain environments;ji;Proc Conf Robot Learn,0

3. Explainable Dynamic Multimodal Variational Autoencoder for the Prediction of Patients with Suspected Central Precocious Puberty

4. Sparse multi-channel variational autoencoder for the joint analysis of heterogeneous data;antelmi;Proc Int Conf Mach Learn,0

5. Auto-Encoding Variational Bayes;kingma;Proc 2nd Int Conf Learn Representations,0

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