LatentVerse aids understanding of multimodal biomedical data embeddings
LatentVerse: A Framework for Understanding Shared and Modality-Specific Information in Multimodal Latent Representations
Machine LearningHuman-Computer Interaction
Summary
Machine learning models often turn complex biomedical data into simpler codes called latent embeddings, which mix information from different sources like images and text. The value of these codes depends on how well we understand what information they hold and how it is organized. The authors developed LatentVerse, a tool that helps people explore and analyze these embeddings, especially by separating shared from source-specific information. This makes it easier for users to interpret and trust the results of AI models handling multiple types of biomedical data.
What this means in practice
- •For biomedical data teams: Evaluate and interpret complex biomedical data embeddings by distinguishing shared versus unique information from different data types in foundation models.
- •For data science teams: Perform integrated analysis on multimodal latent representations to improve the interpretability and quality assessment of data-driven models.
Authors
Majd Alafrange, Samuel Friedman, John Kitonyo, Sana Tonekaboni, Mahnaz Maddah
Abstract
Latent embeddings have become a central data abstraction in modern machine learning, especially in biomedicine, where foundation models are increasingly used to encode multimodal data like clinical text, medical images, omics, and physiological signals. However, the utility and value of these representations depends on understanding their quality, structure, and the information they encode. Existing analysis workflows for evaluating representations remain fragmented across custom scripts, isolated metrics, and most importantly lack multimodal analysis, limiting accessibility and reproducibility. We present LatentVerse, a representation analysis resource that combines a web-based visual analytics platform for accessible, report-driven exploration with a command-line interface for scalable technical workflows. LatentVerse unifies diagnostics for various representation quality metrics and extends to multimodal settings by decomposing embeddings into shared and modality-specific components. We evaluate LatentVerse through controlled unimodal and multimodal simulations, discovery-oriented analyses on real biomedical embeddings, and a user study across diverse use cases. By supporting thorough and interpretable evaluation of latent spaces, LatentVerse makes foundation model representations more understandable in biomedical and data science applications.