End-to-end training of deep probabilistic CCA on paired biomedical observations

Gregory Gundersen, Bianca Dumitrascu, Jordan T. Ash, Barbara E. Engelhardt

Research output: Contribution to conferencePaper

Abstract

Medical pathology images are visually evaluated by experts for disease diagnosis, but the connection between image features and the state of the cells in an image is typically unknown. To understand this relationship, we develop a multimodal modeling and inference framework that estimates shared latent structure of joint gene expression levels and medical image features. Our method is built around probabilistic canonical correlation analysis (PCCA), which is fit to image embeddings that are learned using convolutional neural networks and linear embeddings of paired gene expression data. We train the model end-to-end so that the PCCA and neural network parameters are estimated simultaneously. We demonstrate the utility of this method in constructing image features that are predictive of gene expression levels on simulated data and the Genotype-Tissue Expression data. We demonstrate that the latent variables are interpretable by disentangling the latent subspace through shared and modality-specific views.

Original languageEnglish (US)
StatePublished - Jan 1 2019
Event35th Conference on Uncertainty in Artificial Intelligence, UAI 2019 - Tel Aviv, Israel
Duration: Jul 22 2019Jul 25 2019

Conference

Conference35th Conference on Uncertainty in Artificial Intelligence, UAI 2019
CountryIsrael
CityTel Aviv
Period7/22/197/25/19

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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  • Cite this

    Gundersen, G., Dumitrascu, B., Ash, J. T., & Engelhardt, B. E. (2019). End-to-end training of deep probabilistic CCA on paired biomedical observations. Paper presented at 35th Conference on Uncertainty in Artificial Intelligence, UAI 2019, Tel Aviv, Israel.