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

arXiv: https://arxiv.org/abs/2411.13147 · HTML: https://mlsanity.com/paper/2411.13147/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 57 references that are in the map.
- First posted on arXiv 20 Nov 2024.

## Builds on (most cited first, 1 of 1)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)

## Built on by (most cited first, 9 of 9)

- [In-context learning for medical image segmentation](https://mlsanity.com/paper/2412.13299/) (2024)
- [UniCT Depth: Event-Image Fusion Based Monocular Depth Estimation with Convolution-Compensated ViT Dual SA Block](https://mlsanity.com/paper/2507.19948/) (2025)
- [MOSAIC: A Multi-View 2.5D Organ Slice Selector with Cross-Attentional Reasoning for Anatomically-Aware CT Localization in Medical Organ Segmentation](https://mlsanity.com/paper/2505.10672/) (2025)
- [PCEvo: Path-Consistent Molecular Representation via Virtual Evolutionary](https://mlsanity.com/paper/2601.19257/) (2026)
- [Beyond the Academic Monoculture: A Unified Framework and Industrial Perspective for Attributed Graph Clustering](https://mlsanity.com/paper/2603.20829/) (2026)
- [Pairwise Similarity Regularization for Semi-supervised Graph Medical Image Segmentation](https://mlsanity.com/paper/2503.12800/) (2025)
- [Brain Network Classification Based on Graph Contrastive Learning and Graph Transformer](https://mlsanity.com/paper/2504.03740/) (2025)
- [Semi-Supervised 3D Medical Segmentation from 2D Natural Images Pretrained Model](https://mlsanity.com/paper/2509.15167/) (2025)
- [CPS4: Class Prompt driven Semi-Supervised Spine Segmentation with Class-specific Consistency Constraint](https://mlsanity.com/paper/2606.15802/) (2026)

## Abstract (arXiv)

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.