Summary of Graphcl: Graph-based Clustering For Semi-supervised Medical Image Segmentation, by Mengzhu Wang et al.
GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation
by Mengzhu Wang, Jiao Li, Houcheng Su, Nan Yin, Liang Yang, Shen Li
First submitted to arxiv on: 20 Nov 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 This paper proposes a novel graph-based approach for semi-supervised learning in medical image segmentation, which leverages both unlabeled data and graph structural information to enhance data utilization efficiency. The proposed method, called GraphCL, jointly models the graph data structure within a unified deep model, outperforming state-of-the-art semi-supervised methods on three standard benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research develops a new way to analyze medical images without needing too many labeled examples. It uses graphs to represent the relationships between different parts of an image and combines this information with unlabeled data to improve segmentation results. The method, called GraphCL, is tested on several well-known datasets and shows better performance than other approaches. |
Keywords
» Artificial intelligence » Image segmentation » Semi supervised
