Latent Space Explorer: Visual Analytics for Multimodal Latent Space
  Exploration
Bum Chul Kwon Samuel Friedman Kai Xu Steven A Lubitz Anthony Philippakis Puneet Batra Patrick T Ellinor Kenney Ng
Machine learning models built on training data with multiple modalities can
reveal new insights that are not accessible through unimodal datasets. For
example, cardiac magnetic resonance images (MRIs) and electrocardiograms (ECGs)
are both known to capture useful information about subjects' cardiovascular
health status. A multimodal machine learning model trained from large datasets
can potentially predict the onset of heart-related diseases and provide novel
medical insights about the cardiovascular system. Despite the potential
benefits, it is difficult for medical experts to explore multimodal
representation models without visual aids and to test the predictive
performance of the models on various subpopulations. To address the challenges,
we developed a visual analytics system called Latent Space Explorer. Latent
Space Explorer provides interactive visualizations that enable users to explore
the multimodal representation of subjects, define subgroups of interest,
interactively decode data with different modalities with the selected subjects,
and inspect the accuracy of the embedding in downstream prediction tasks. A
user study was conducted with medical experts and their feedback provided
useful insights into how Latent Space Explorer can help their analysis and
possible new direction for further development in the medical domain.