Promoting cross-modal representations to improve multimodal foundation models for physiological signals (2410.16424v1)

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.

• The paper introduces a masked autoencoding approach with modality drop to integrate diverse physiological signals, enhancing model accuracy in tasks like sleep staging and age detection.
• The research adapts vision transformer architecture, enabling effective fusion of modalities such as EEG, ECG, EMG, and EOG through joint encoding layers.
• Results indicate that cross-modal learning produces robust representations, offering promising implications for personalized healthcare and efficient wearable technology.

An Analysis of Cross-Modal Representations in Multimodal Models for Physiological Signals

The paper "Promoting Cross-Modal Representations to Improve Multimodal Foundation Models for Physiological Signals" presents a rigorous exploration of multimodal learning with physiological signals, leveraging the capability of foundation models to unify disparate data types. The authors focus on the PhysioNet 2018 dataset, utilizing a masked autoencoding objective to pretrain models capable of handling EEG, EMG, EOG, and ECG signal data effectively across multiple downstream tasks.

Objective and Methodology

Physiological signals present unique challenges in the field of multimodal learning, particularly due to data heterogeneity, privacy concerns, and inter-subject variability. Addressing these, the paper pretrains a multimodal model using masked autoencoding with augmented cross-modal integration through modality drop techniques. This method promotes the integration of different signal types, hypothesizing that cross-modal reconstruction enhances the utility of learned representations. By including modulation in the input space, the paper demonstrates improvements in model performance on tasks like sleep staging, age classification, and arousal detection.

The authors apply the vision transformer architecture adapted to multimodal signal processing. The model's modular design allows for modality-specific input through tokenizers, followed by joint encoding layers designed to maximize cross-modal information fusion.

Results and Performance

The authors' approach reveals substantial benefits from pretraining and testing various strategies against unimodal and standard multimodal techniques. The model trained with modality drop indicated a marked improvement in downstream performance, especially noticeable in the context of limited training data environments. This suggests that cross-modal learning, nurtured via dropout strategies, fosters more robust representations transferable across tasks and datasets.

Quantitative evaluation reveals that the combined modality approach surpasses or matches unimodal models, particularly excelling in age and arousal detection tasks. Balanced accuracy scores reinforce that the utility of these representations spans a variety of tasks, emphasizing the model's potential applicability in real-world scenarios where diverse physiological data must be integrated and interpreted.

Attention Mechanisms and Representation Analysis

The paper also provides insights into the inner workings of the model, examining how attention weights evolve throughout the network layers. Notably, attention becomes more temporally and cross-modally aligned via the chosen pretraining strategy, indicating effective modality integration. Additionally, the relative source variance (RSV) analysis shows that embeddings in models augmented with modality drop have units uniformly shared across modalities, enhancing cross-modal representation balance.

Implications and Future Prospects

The architecture and strategies deployed in this work open new pathways for deploying foundation models in healthcare, particularly as sensor ubiquity and data quantity grow. The findings advocate for pretraining approaches that explicitly engineer cross-modal synergy, particularly for applications in wearable technologies where model size and efficiency are critical.

Future developments may benefit from refining these models with domain-specific contrastive learning objectives, potentially enhancing performance further. Moreover, expanding tasks and datasets will bolster validations of these models' versatility, making them invaluable assets for diverse applications in personalized healthcare and beyond.

This paper presents a meticulously validated approach to multimodal model training, demonstrating promising strides toward tackling the inherent challenges in healthcare data interpretation. The results underscore the potential and efficacy of integrating modality drop and cross-modal reconstruction, illumining paths for future exploration and application in AI-driven healthcare solutions.