Summary of Adapt: Multimodal Learning For Detecting Physiological Changes Under Missing Modalities, by Julie Mordacq et al.
ADAPT: Multimodal Learning for Detecting Physiological Changes under Missing Modalities
by Julie Mordacq, Leo Milecki, Maria Vakalopoulou, Steve Oudot, Vicky Kalogeiton
First submitted to arxiv on: 4 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 presents a novel multimodal framework called AnchoreD (ADAPT) that addresses two key challenges in integrating biomedical signals with imaging or video modalities. ADAPT is designed to balance the contributions of multiple modalities, even when data is limited, and handle missing modalities. The framework consists of two main components: aligning all modalities in the space of the strongest modality (the anchor) to learn a joint embedding space, and a Masked Multimodal Transformer that leverages inter- and intra-modality correlations while handling missing modalities. The authors demonstrate the effectiveness of ADAPT by detecting physiological changes in two real-life scenarios: stress induction in individuals and fighter pilots’ loss of consciousness due to G-forces. They validate the generalizability of ADAPT through extensive experiments on two datasets, achieving state-of-the-art performance and showcasing its robustness across various modality scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists developed a new way to combine different types of data from medical tests and images with patient information. This helps doctors make better decisions when patients are stressed or experiencing loss of consciousness due to physical forces like G-forces. The team created a special kind of computer model that can handle missing or incomplete data and learn from many different sources at once. They tested this model on two real-life scenarios and found it worked much better than other methods. |
Keywords
* Artificial intelligence * Embedding space * Transformer
