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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.00725 |
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| _version_ | 1866913569796259840 |
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| author | Wenderoth, Laura |
| author_facet | Wenderoth, Laura |
| contents | This paper investigates the MM dynamics approach proposed by Han et al. (2022) for multi-modal fusion in biomedical classification tasks. The MM dynamics algorithm integrates feature-level and modality-level informativeness to dynamically fuse modalities for improved classification performance. However, our analysis reveals several limitations and challenges in replicating and extending the results of MM dynamics. We found that feature informativeness improves performance and explainability, while modality informativeness does not provide significant advantages and can lead to performance degradation. Based on these results, we have extended feature informativeness to image data, resulting in the development of Image MM dynamics. Although this approach showed promising qualitative results, it did not outperform baseline methods quantitatively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_00725 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring Multi-Modality Dynamics: Insights and Challenges in Multimodal Fusion for Biomedical Tasks Wenderoth, Laura Machine Learning This paper investigates the MM dynamics approach proposed by Han et al. (2022) for multi-modal fusion in biomedical classification tasks. The MM dynamics algorithm integrates feature-level and modality-level informativeness to dynamically fuse modalities for improved classification performance. However, our analysis reveals several limitations and challenges in replicating and extending the results of MM dynamics. We found that feature informativeness improves performance and explainability, while modality informativeness does not provide significant advantages and can lead to performance degradation. Based on these results, we have extended feature informativeness to image data, resulting in the development of Image MM dynamics. Although this approach showed promising qualitative results, it did not outperform baseline methods quantitatively. |
| title | Exploring Multi-Modality Dynamics: Insights and Challenges in Multimodal Fusion for Biomedical Tasks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2411.00725 |