Summary of A Systematic Review Of Intermediate Fusion in Multimodal Deep Learning For Biomedical Applications, by Valerio Guarrasi et al.
A Systematic Review of Intermediate Fusion in Multimodal Deep Learning for Biomedical Applications
by Valerio Guarrasi, Fatih Aksu, Camillo Maria Caruso, Francesco Di Feola, Aurora Rofena, Filippo Ruffini, Paolo Soda
First submitted to arxiv on: 2 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Deep learning has transformed biomedical research by providing advanced methods to handle complex data. Multimodal deep learning (MDL) takes it further by integrating various data types, such as imaging, text, and genetic information, resulting in more accurate predictive models. Intermediate fusion stands out among early and late fusion methods for its ability to effectively combine modality-specific features during the learning process. This review analyzes current intermediate fusion methods in biomedical applications, investigating employed techniques, faced challenges, and potential future directions. A structured notation is introduced to enhance understanding and application of these methods beyond the biomedical domain. The findings aim to support researchers, healthcare professionals, and the broader deep learning community in developing more sophisticated multimodal models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning has greatly improved biomedical research by allowing it to handle complex data effectively. Multimodal deep learning combines different types of data like imaging, text, and genes to make better predictions. One way to combine this data is called intermediate fusion. This review looks at how researchers are using intermediate fusion in biomedical applications, what challenges they’re facing, and where things might go from here. It also provides a simple way to understand and use these methods for other areas beyond just biomedical research. |
Keywords
* Artificial intelligence * Deep learning
