GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation

Autor:	Wang, Mengzhu, Li, Jiao, Su, Houcheng, Yin, Nan, Yang, Liang, Li, Shen
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence 68T07 92C55 62H35 I.2.6 I.4.10 J.3
Druh dokumentu:	Working Paper
Popis:	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. Comment: 9page
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.13147 Zobrazit plný text záznamu View this record from Arxiv