Summary of Glfnet: Global-local (frequency) Filter Networks For Efficient Medical Image Segmentation, by Athanasios Tragakis et al.
GLFNET: Global-Local (frequency) Filter Networks for efficient medical image segmentation
by Athanasios Tragakis, Qianying Liu, Chaitanya Kaul, Swalpa Kumar Roy, Hang Dai, Fani Deligianni, Roderick Murray-Smith, Daniele Faccio
First submitted to arxiv on: 1 Mar 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 The proposed Global-Local Filter Network (GLFNet) architecture achieves state-of-the-art performance in medical image segmentation by replacing the self-attention mechanism with a combination of global-local filter blocks. This optimization improves model efficiency, leveraging global filters to extract features from the entire feature map and local filters that adaptively capture 4×4 patches of the same feature map, incorporating restricted scale information. The frequency domain-based feature extraction enables faster computations, while the fusion of spatial and frequency spaces creates an efficient model with reduced complexity, data requirements, and performance. GLFNet outperforms existing methods on three benchmark datasets, demonstrating its effectiveness in medical image segmentation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to analyze medical images called Global-Local Filter Network (GLFNet). This method helps doctors better understand medical images by using a combination of global and local information. The global part looks at the whole image, while the local part focuses on small areas. This allows for faster processing and better results. The team tested GLFNet on several sets of medical images and found that it outperformed other methods. |
Keywords
» Artificial intelligence » Feature extraction » Feature map » Image segmentation » Optimization » Self attention
