To develop a performance and model agnostic metric for measuring modality contribution in multimodal medical datasets, where 'model agnostic' refers to methods that do not depend on the specific architecture of the model used.
Key Findings:
Existing interpretability methods for multimodal data are underexplored and lack quantification of modality importance, limiting their effectiveness.
The developed metric can detect unimodal collapses, indicating a model's reliance on a single modality, which is crucial for understanding model behavior.
The metric is tested on various medical datasets, demonstrating its applicability in clinical tasks and providing a foundation for future research.
Interpretation:
The new metric enhances the understanding of how different modalities contribute to model performance, facilitating better model comparisons and trust in AI systems, which is essential for their adoption in clinical settings.
Limitations:
The metric's effectiveness may vary depending on the specific characteristics of the datasets used, such as data quality and modality diversity.
Further validation is needed across diverse medical applications to establish generalizability and robustness.
Conclusion:
The introduction of a model and performance agnostic metric for modality contribution is a significant step towards improving interpretability in multimodal medical deep learning, with the potential to enhance trust and integration of AI in clinical practice.
