STORM: Strategic Orchestration of Modalities for Rare Event Classification

TL;DR

STORM is an entropy-based algorithm that systematically selects the most informative modalities for rare event classification, improving efficiency and accuracy in biomedical AI applications.

Contribution

The paper introduces STORM, a novel systematic modality selection algorithm that enhances rare event classification by evaluating information content and discriminative features.

Findings

Effective in seizure onset zone detection

Improves classification accuracy with fewer modalities

Demonstrates efficiency in biomedical analysis

Abstract

In domains such as biomedical, expert insights are crucial for selecting the most informative modalities for artificial intelligence (AI) methodologies. However, using all available modalities poses challenges, particularly in determining the impact of each modality on performance and optimizing their combinations for accurate classification. Traditional approaches resort to manual trial and error methods, lacking systematic frameworks for discerning the most relevant modalities. Moreover, although multi-modal learning enables the integration of information from diverse sources, utilizing all available modalities is often impractical and unnecessary. To address this, we introduce an entropy-based algorithm STORM to solve the modality selection problem for rare event. This algorithm systematically evaluates the information content of individual modalities and their combinations, identifying the most discriminative features essential for rare class classification tasks. Through seizure onset zone detection case study, we demonstrate the efficacy of our algorithm in enhancing classification performance. By selecting useful subset of modalities, our approach paves the way for more efficient AI-driven biomedical analyses, thereby advancing disease diagnosis in clinical settings.