Summary of Mda: An Interpretable and Scalable Multi-modal Fusion Under Missing Modalities and Intrinsic Noise Conditions, by Lin Fan et al.
MDA: An Interpretable and Scalable Multi-Modal Fusion under Missing Modalities and Intrinsic Noise Conditions
by Lin Fan, Yafei Ou, Cenyang Zheng, Pengyu Dai, Tamotsu Kamishima, Masayuki Ikebe, Kenji Suzuki, Xun Gong
First submitted to arxiv on: 15 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces the Modal-Domain Attention (MDA) model to address challenges in multi-modal learning, which has shown exceptional performance in medical applications by integrating diverse information for diagnostic evidence. Specifically, MDA constructs linear relationships between modalities through continuous attention, adaptively allocating dynamic attention to different modalities and reducing attention to low-correlation data or modalities with inherent noise. This results in maintaining state-of-the-art (SOTA) performance across various tasks on multiple public datasets. Furthermore, the study’s observations indicate that MDA aligns with established clinical diagnostic imaging gold standards and has promise as a reference for pathologies where these standards are not yet clearly defined. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to make multi-modal learning work better. Right now, it’s really good at combining different kinds of medical information to help diagnose diseases. But there are some problems with this approach, like when data from one type of test doesn’t match up well with data from another type of test. The authors introduce a new model called Modal-Domain Attention (MDA) that can deal with these challenges. It’s really good at picking out the most important information and ignoring things that aren’t helpful. This could be especially useful for diseases where doctors are still trying to figure out what the signs look like. |
Keywords
* Artificial intelligence * Attention * Multi modal
