Flex-MoE: Modeling Arbitrary Modality Combination via the Flexible Mixture-of-Experts (2410.08245v2)

Abstract: Multimodal learning has gained increasing importance across various fields, offering the ability to integrate data from diverse sources such as images, text, and personalized records, which are frequently observed in medical domains. However, in scenarios where some modalities are missing, many existing frameworks struggle to accommodate arbitrary modality combinations, often relying heavily on a single modality or complete data. This oversight of potential modality combinations limits their applicability in real-world situations. To address this challenge, we propose Flex-MoE (Flexible Mixture-of-Experts), a new framework designed to flexibly incorporate arbitrary modality combinations while maintaining robustness to missing data. The core idea of Flex-MoE is to first address missing modalities using a new missing modality bank that integrates observed modality combinations with the corresponding missing ones. This is followed by a uniquely designed Sparse MoE framework. Specifically, Flex-MoE first trains experts using samples with all modalities to inject generalized knowledge through the generalized router ($\mathcal{G}$-Router). The $\mathcal{S}$-Router then specializes in handling fewer modality combinations by assigning the top-1 gate to the expert corresponding to the observed modality combination. We evaluate Flex-MoE on the ADNI dataset, which encompasses four modalities in the Alzheimer's Disease domain, as well as on the MIMIC-IV dataset. The results demonstrate the effectiveness of Flex-MoE highlighting its ability to model arbitrary modality combinations in diverse missing modality scenarios. Code is available at https://github.com/UNITES-Lab/flex-moe.

• The paper introduces Flex-MoE, a flexible Mixture-of-Experts framework designed to effectively handle arbitrary combinations of available data modalities, even when some are missing.
• A novel Missing Modality Bank addresses incomplete data by generating embeddings for absent modalities, using knowledge from observed combinations to ensure models function without requiring full datasets.
• Flex-MoE utilizes a Sparse Mixture-of-Experts design where experts are first trained for generalization on complete data and then specialized for specific modality subsets using a top-1 gating approach.

Overview of Flex-MoE: Modeling Arbitrary Modality Combination via the Flexible Mixture-of-Experts

The paper addresses a critical challenge in multimodal learning: the frequent absence of certain data modalities in real-world applications, particularly in medical domains where patient records might consist of images, clinical data, genetic information, and more. Traditional models often struggle to effectively handle these missing modalities, typically requiring complete data or heavily favoring a single modality. This paper introduces Flex-MoE (Flexible Mixture-of-Experts), an innovative framework designed to adaptively integrate arbitrary combinations of available modalities while demonstrating resilience to their absence.

Key Contributions

1. Missing Modality Bank:
   • A novel missing modality bank is introduced to address incomplete data scenarios by generating embeddings for absent modalities. This bank derives knowledge from observed combinations and augments them with inferred embeddings, ensuring models can still function effectively without complete data.
2. Sparse Mixture-of-Experts (SMoE) Design:
   • The paper leverages a Sparse Mixture-of-Experts framework, which is crafted in a two-step process.
   • Generalization ("$\mathcal{G}$-Router"): All experts within the SMoE are initially trained on completely observed samples to spread generalized knowledge.
   • Specialization ("$\mathcal{S}$-Router"): Each expert is then tailored to specific modality combinations, ensuring they can handle fewer modalities robustly. This process utilizes a top-1 gating approach, significantly enhancing the model's adaptability and precision with partial data.
3. Empirical Evaluation:
   • The Flex-MoE framework was assessed using the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the MIMIC-IV datasets, involving various modalities. The results demonstrated its superiority in handling diverse combinations of available modalities compared to existing methods, substantiating its versatility and efficacy in realistic medical scenarios.

Methodological Insights

The Flex-MoE strategically tackles missing data challenges by not only interpolating missing modalities with plausible embeddings from the modality bank but also by crafting a dynamic interaction network through the SMoE framework. The network's generalization capability, followed by targeted specialization, ensures that the model can leverage complete data scenarios effectively while being fully prepared for any modality set scenarios. This approach significantly reduces the need for imputation, thereby preserving the integrity and reliability of the analysis.

Implications and Future Directions

Flex-MoE's framework significantly broadens the applicability of multimodal learning models across fields that inherently suffer from incomplete data acquisition, such as healthcare. The model's adaptability could be instrumental in improving diagnostic accuracy and prediction when only partial patient data is available, leading to better-informed decisions and potentially enhancing patient outcomes.

The future of AI could see further developments in this area, building on Flex-MoE's ability to handle diverse data availability scenarios. Extending this approach to even larger and more complex datasets, possibly utilizing advanced architectures or integrating with real-time data streams, presents exciting opportunities for research. Moreover, the development of standardized missing modality banks could bolster generalization across various applications, making Flex-MoE versatile across different domains beyond healthcare.

In summary, Flex-MoE marks a significant advancement in the field of multimodal learning, particularly regarding the integration of incomplete data modalities. Its approach provides a structured and effective mechanism for addressing the practical challenges posed by missing data, setting the stage for further innovations in AI and machine learning applications.