STORM: Strategic Orchestration of Modalities for Rare Event Classification
This work addresses the challenge of inefficient modality selection for rare event classification in biomedical AI, offering a systematic framework to improve disease diagnosis in clinical settings, though it is incremental as it builds on multi-modal learning.
The paper tackled the problem of selecting the most informative modalities for rare event classification in biomedical domains, introducing the STORM algorithm which systematically evaluates modalities to enhance classification performance, as demonstrated in a seizure onset zone detection case study.
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.