Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.13147 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915030603137024 |
|---|---|
| author | Wang, Mengzhu Li, Jiao Su, Houcheng Yin, Nan Yang, Liang Li, Shen |
| author_facet | Wang, Mengzhu Li, Jiao Su, Houcheng Yin, Nan Yang, Liang Li, Shen |
| contents | Semi-supervised learning (SSL) has made notable advancements in medical image segmentation (MIS), particularly in scenarios with limited labeled data and significantly enhancing data utilization efficiency. Previous methods primarily focus on complex training strategies to utilize unlabeled data but neglect the importance of graph structural information. Different from existing methods, we propose a graph-based clustering for semi-supervised medical image segmentation (GraphCL) by jointly modeling graph data structure in a unified deep model. The proposed GraphCL model enjoys several advantages. Firstly, to the best of our knowledge, this is the first work to model the data structure information for semi-supervised medical image segmentation (SSMIS). Secondly, to get the clustered features across different graphs, we integrate both pairwise affinities between local image features and raw features as inputs. Extensive experimental results on three standard benchmarks show that the proposed GraphCL algorithm outperforms state-of-the-art semi-supervised medical image segmentation methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_13147 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Wang, Mengzhu Li, Jiao Su, Houcheng Yin, Nan Yang, Liang Li, Shen Computer Vision and Pattern Recognition Artificial Intelligence 68T07, 92C55, 62H35 I.2.6; I.4.10; J.3 Semi-supervised learning (SSL) has made notable advancements in medical image segmentation (MIS), particularly in scenarios with limited labeled data and significantly enhancing data utilization efficiency. Previous methods primarily focus on complex training strategies to utilize unlabeled data but neglect the importance of graph structural information. Different from existing methods, we propose a graph-based clustering for semi-supervised medical image segmentation (GraphCL) by jointly modeling graph data structure in a unified deep model. The proposed GraphCL model enjoys several advantages. Firstly, to the best of our knowledge, this is the first work to model the data structure information for semi-supervised medical image segmentation (SSMIS). Secondly, to get the clustered features across different graphs, we integrate both pairwise affinities between local image features and raw features as inputs. Extensive experimental results on three standard benchmarks show that the proposed GraphCL algorithm outperforms state-of-the-art semi-supervised medical image segmentation methods. |
| title | GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence 68T07, 92C55, 62H35 I.2.6; I.4.10; J.3 |
| url | https://arxiv.org/abs/2411.13147 |