Summary of A Review Of Deep Learning-based Information Fusion Techniques For Multimodal Medical Image Classification, by Yihao Li et al.
A review of deep learning-based information fusion techniques for multimodal medical image classification
by Yihao Li, Mostafa El Habib Daho, Pierre-Henri Conze, Rachid Zeghlache, Hugo Le Boité, Ramin Tadayoni, Béatrice Cochener, Mathieu Lamard, Gwenolé Quellec
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 In a comprehensive review, researchers delve into deep learning-based multimodal fusion techniques for medical image classification tasks. They analyze the relationships among clinical modalities and outline three main fusion schemes: input, intermediate (including single-level, hierarchical, and attention-based), and output fusion. The study evaluates the performance of these techniques to provide insights on suitable network architectures for various scenarios and domains. It also discusses challenges in network architecture selection, handling incomplete data, and limitations of multimodal fusion. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This review explores how deep learning can help combine medical images from different sources to improve diagnosis. Researchers have developed ways to “fuse” these images together using techniques like input, intermediate, or output fusion. The study looks at which methods work best for different tasks and domains. It also talks about the challenges of choosing the right network architecture and handling missing data. |
Keywords
» Artificial intelligence » Attention » Deep learning » Image classification
