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

• URL: http://arxiv.org/abs/2411.13147v2
• Date: Fri, 22 Nov 2024 09:18:20 GMT
• Title: GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation
• Authors: Mengzhu Wang, Jiao Li, Houcheng Su, Nan Yin, Liang Yang, Shen Li,
• Abstract summary: We propose a graph-based clustering for semi-supervised medical image segmentation (GraphCL) by jointly modeling graph data structure in a unified deep model. We show that the proposed GraphCL algorithm outperforms state-of-the-art semi-supervised medical image segmentation methods.
• Score: 20.43162035511489
• License:
• Abstract: 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.

Related papers

• UnSegMedGAT: Unsupervised Medical Image Segmentation using Graph Attention Networks Clustering [10.862430265350804]
  We propose an unsupervised segmentation framework using a pre-trained Dino-ViT. We leverage the inherent graph structure within the image to realize a significant performance gain for segmentation in medical images. Our method achieves state-of-the-art performance, even significantly surpassing or matching that of existing (semi) technique such as MedSAM.
  arXiv  Detail & Related papers  (2024-11-04T10:42:21Z)
• Enhancing Multimodal Medical Image Classification using Cross-Graph Modal Contrastive Learning [5.660131312162423]
  This paper proposes a novel Cross-Graph Modal Contrastive Learning framework for multimodal medical image classification. The proposed approach is evaluated on two datasets: a Parkinson's disease (PD) dataset and a public melanoma dataset. Results demonstrate that CGMCL outperforms conventional unimodal methods in accuracy, interpretability, and early disease prediction.
  arXiv  Detail & Related papers  (2024-10-23T01:25:25Z)
• UnSeGArmaNet: Unsupervised Image Segmentation using Graph Neural Networks with Convolutional ARMA Filters [10.940349832919699]
  We propose an unsupervised segmentation framework with a pre-trained ViT. By harnessing the graph structure inherent within the image, the proposed method achieves a notable performance in segmentation. The proposed method provides state-of-the-art performance (even comparable to supervised methods) on benchmark image segmentation datasets.
  arXiv  Detail & Related papers  (2024-10-08T15:10:09Z)
• Cross-model Mutual Learning for Exemplar-based Medical Image Segmentation [25.874281336821685]
  Cross-model Mutual learning framework for Exemplar-based Medical image (CMEMS) We introduce a novel Cross-model Mutual learning framework for Exemplar-based Medical image (CMEMS)
  arXiv  Detail & Related papers  (2024-04-18T00:18:07Z)
• Deep Contrastive Graph Learning with Clustering-Oriented Guidance [61.103996105756394]
  Graph Convolutional Network (GCN) has exhibited remarkable potential in improving graph-based clustering. Models estimate an initial graph beforehand to apply GCN. Deep Contrastive Graph Learning (DCGL) model is proposed for general data clustering.
  arXiv  Detail & Related papers  (2024-02-25T07:03:37Z)
• Learnable Weight Initialization for Volumetric Medical Image Segmentation [66.3030435676252]
  We propose a learnable weight-based hybrid medical image segmentation approach. Our approach is easy to integrate into any hybrid model and requires no external training data. Experiments on multi-organ and lung cancer segmentation tasks demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-06-15T17:55:05Z)
• Effective and Efficient Graph Learning for Multi-view Clustering [173.8313827799077]
  We propose an effective and efficient graph learning model for multi-view clustering. Our method exploits the view-similar between graphs of different views by the minimization of tensor Schatten p-norm. Our proposed algorithm is time-economical and obtains the stable results and scales well with the data size.
  arXiv  Detail & Related papers  (2021-08-15T13:14:28Z)
• Semi-supervised Superpixel-based Multi-Feature Graph Learning for Hyperspectral Image Data [0.0]
  We present a novel framework for the classification of Hyperspectral Image (HSI) data in light of a very limited amount of labelled data. We propose a multi-stage edge-efficient semi-supervised graph learning framework for HSI data.
  arXiv  Detail & Related papers  (2021-04-27T15:36:26Z)
• Diversified Multiscale Graph Learning with Graph Self-Correction [55.43696999424127]
  We propose a diversified multiscale graph learning model equipped with two core ingredients. A graph self-correction (GSC) mechanism to generate informative embedded graphs, and a diversity boosting regularizer (DBR) to achieve a comprehensive characterization of the input graph. Experiments on popular graph classification benchmarks show that the proposed GSC mechanism leads to significant improvements over state-of-the-art graph pooling methods.
  arXiv  Detail & Related papers  (2021-03-17T16:22:24Z)
• Model-Agnostic Graph Regularization for Few-Shot Learning [60.64531995451357]
  We present a comprehensive study on graph embedded few-shot learning. We introduce a graph regularization approach that allows a deeper understanding of the impact of incorporating graph information between labels. Our approach improves the performance of strong base learners by up to 2% on Mini-ImageNet and 6.7% on ImageNet-FS.
  arXiv  Detail & Related papers  (2021-02-14T05:28:13Z)
• Structured Graph Learning for Clustering and Semi-supervised Classification [74.35376212789132]
  We propose a graph learning framework to preserve both the local and global structure of data. Our method uses the self-expressiveness of samples to capture the global structure and adaptive neighbor approach to respect the local structure. Our model is equivalent to a combination of kernel k-means and k-means methods under certain condition.
  arXiv  Detail & Related papers  (2020-08-31T08:41:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.