Local Clustering for Lung Cancer Image Classification via Sparse Solution Technique

• URL: http://arxiv.org/abs/2407.08800v1
• Date: Thu, 11 Jul 2024 18:18:32 GMT
• Title: Local Clustering for Lung Cancer Image Classification via Sparse Solution Technique
• Authors: Jackson Hamel, Ming-Jun Lai, Zhaiming Shen, Ye Tian,
• Abstract summary: We view images as the vertices in a weighted graph and the similarity between a pair of images as the edges in the graph. Our approach is significantly more efficient and either favorable or equally effective compared with other state-of-the-art approaches.
• Score: 1.07793546088014
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we propose to use a local clustering approach based on the sparse solution technique to study the medical image, especially the lung cancer image classification task. We view images as the vertices in a weighted graph and the similarity between a pair of images as the edges in the graph. The vertices within the same cluster can be assumed to share similar features and properties, thus making the applications of graph clustering techniques very useful for image classification. Recently, the approach based on the sparse solutions of linear systems for graph clustering has been found to identify clusters more efficiently than traditional clustering methods such as spectral clustering. We propose to use the two newly developed local clustering methods based on sparse solution of linear system for image classification. In addition, we employ a box spline-based tight-wavelet-framelet method to clean these images and help build a better adjacency matrix before clustering. The performance of our methods is shown to be very effective in classifying images. Our approach is significantly more efficient and either favorable or equally effective compared with other state-of-the-art approaches. Finally, we shall make a remark by pointing out two image deformation methods to build up more artificial image data to increase the number of labeled images.

Related papers

• Patch-Based Deep Unsupervised Image Segmentation using Graph Cuts [0.0]
  We propose a patch-based unsupervised image segmentation strategy that bridges advances in unsupervised feature extraction with the algorithmic help of classical graph-based methods. We show that a simple convolutional neural network, trained to classify image patches, naturally leads to a state-of-the-art fully-convolutional unsupervised pixel-level segmenter.
  arXiv  Detail & Related papers  (2023-11-01T19:59:25Z)
• Reinforcement Graph Clustering with Unknown Cluster Number [91.4861135742095]
  We propose a new deep graph clustering method termed Reinforcement Graph Clustering. In our proposed method, cluster number determination and unsupervised representation learning are unified into a uniform framework. In order to conduct feedback actions, the clustering-oriented reward function is proposed to enhance the cohesion of the same clusters and separate the different clusters.
  arXiv  Detail & Related papers  (2023-08-13T18:12:28Z)
• DeepCut: Unsupervised Segmentation using Graph Neural Networks Clustering [6.447863458841379]
  This study introduces a lightweight Graph Neural Network (GNN) to replace classical clustering methods. Unlike existing methods, our GNN takes both the pair-wise affinities between local image features and the raw features as input. We demonstrate how classical clustering objectives can be formulated as self-supervised loss functions for training an image segmentation GNN.
  arXiv  Detail & Related papers  (2022-12-12T12:31:46Z)
• Improving Image Clustering through Sample Ranking and Its Application to remote--sensing images [14.531733039462058]
  We propose a novel method by first ranking samples within each cluster based on the confidence in their belonging to the current cluster. For ranking the samples, we developed a method for computing the likelihood of samples belonging to the current clusters based on whether they are situated in densely populated neighborhoods. We show that our method can be effectively applied to remote-sensing images.
  arXiv  Detail & Related papers  (2022-09-26T12:10:02Z)
• Semantic-Enhanced Image Clustering [6.218389227248297]
  We propose to investigate the task of image clustering with the help of a visual-language pre-training model. How to map images to a proper semantic space and how to cluster images from both image and semantic spaces are two key problems. We propose a method to map the given images to a proper semantic space first and efficient methods to generate pseudo-labels according to the relationships between images and semantics.
  arXiv  Detail & Related papers  (2022-08-21T09:04:21Z)
• 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)
• Clustering by Maximizing Mutual Information Across Views [62.21716612888669]
  We propose a novel framework for image clustering that incorporates joint representation learning and clustering. Our method significantly outperforms state-of-the-art single-stage clustering methods across a variety of image datasets.
  arXiv  Detail & Related papers  (2021-07-24T15:36:49Z)
• Graph Contrastive Clustering [131.67881457114316]
  We propose a novel graph contrastive learning framework, which is then applied to the clustering task and we come up with the Graph Constrastive Clustering(GCC) method. Specifically, on the one hand, the graph Laplacian based contrastive loss is proposed to learn more discriminative and clustering-friendly features. On the other hand, a novel graph-based contrastive learning strategy is proposed to learn more compact clustering assignments.
  arXiv  Detail & Related papers  (2021-04-03T15:32:49Z)
• Scalable Hierarchical Agglomerative Clustering [65.66407726145619]
  Existing scalable hierarchical clustering methods sacrifice quality for speed. We present a scalable, agglomerative method for hierarchical clustering that does not sacrifice quality and scales to billions of data points.
  arXiv  Detail & Related papers  (2020-10-22T15:58:35Z)
• 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.