Summary of Unsegmedgat: Unsupervised Medical Image Segmentation Using Graph Attention Networks Clustering, by A. Mudit Adityaja et al.
UnSegMedGAT: Unsupervised Medical Image Segmentation using Graph Attention Networks Clustering
by A. Mudit Adityaja, Saurabh J. Shigwan, Nitin Kumar
First submitted to arxiv on: 4 Nov 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 A novel unsupervised segmentation framework for medical images is proposed, leveraging the power of Vision transformers (ViT) and Graph Attention Networks (GAT). The method uses a pre-trained Dino-ViT to realize significant performance gains in medical image segmentation. A modularity-based loss function is introduced to effectively capture the inherent graph topology within the image. Experimental results demonstrate state-of-the-art performance, surpassing or matching existing semi-supervised techniques on two challenging medical image datasets: ISIC-2018 and CVC-ColonDB. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a new way to analyze medical images without needing labeled data. A special kind of AI model called Vision transformers is used, along with another tool called Graph Attention Networks. This combination helps the model learn from the structure within the image, making it better at segmenting different parts. The results show that this approach works really well on two important medical image datasets. |
Keywords
» Artificial intelligence » Attention » Image segmentation » Loss function » Semi supervised » Unsupervised » Vit
