Promoting cross-modal representations to improve multimodal foundation models for physiological signals

TL;DR

This paper explores pretraining strategies for multimodal healthcare models using physiological signals, emphasizing cross-modal reconstruction and modality dropout to improve downstream task performance and representation quality.

Contribution

It introduces a masked autoencoding pretraining approach with modality dropout and analyzes the impact of cross-modal objectives on model representations in healthcare data.

Findings

Cross-modal reconstruction improves downstream task performance.

Modality dropout enhances model robustness and generalization.

Pretraining leads to more cross-modal and temporally aligned attention weights.

Abstract

Many healthcare applications are inherently multimodal, involving several physiological signals. As sensors for these signals become more common, improving machine learning methods for multimodal healthcare data is crucial. Pretraining foundation models is a promising avenue for success. However, methods for developing foundation models in healthcare are still in early exploration and it is unclear which pretraining strategies are most effective given the diversity of physiological signals. This is partly due to challenges in multimodal health data: obtaining data across many patients is difficult and costly, there is a lot of inter-subject variability, and modalities are often heterogeneously informative across downstream tasks. Here, we explore these challenges in the PhysioNet 2018 dataset. We use a masked autoencoding objective to pretrain a multimodal model. We show that the model learns representations that can be linearly probed for a diverse set of downstream tasks. We hypothesize that cross-modal reconstruction objectives are important for successful multimodal training, as they encourage the model to integrate information across modalities. We demonstrate that modality dropout in the input space improves performance across downstream tasks. We also find that late-fusion models pretrained with contrastive learning objectives are less effective across multiple tasks. Finally, we analyze the model's representations, showing that attention weights become more cross-modal and temporally aligned with our pretraining strategy. The learned embeddings also become more distributed in terms of the modalities encoded by each unit. Overall, our work demonstrates the utility of multimodal foundation models with health data, even across diverse physiological data sources. We further argue that explicit methods for inducing cross-modality may enhance multimodal pretraining strategies.