Multiview Robust Graph-Based Clustering for Cancer Subtype Identification

Shi, Xiaohui; Liang, Cheng; Wang, Hong

doi:10.1109/tcbb.2022.3143897

Cited by 9 publications

(3 citation statements)

References 44 publications

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“…This can result in unnecessary anxiety, inconvenient follow-up care, extra imaging tests, and sometimes a need for tissue sampling (often a needle biopsy) [5,6]. Additionally, machine learning techniques have the potential to improve the process of evaluating multiple-view radiology images based on graph-based clustering techniques [7][8][9][10]. Deep learning as a subset of machine learning in recent years has revolutionized the interpretation of diagnostic imaging studies [11].…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Detection in Mammography Images: A CNN-Based Approach with Feature Selection

Jafari

Karami

2023

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The prompt and accurate diagnosis of breast lesions, including the distinction between cancer, non-cancer, and suspicious cancer, plays a crucial role in the prognosis of breast cancer. In this paper, we introduce a novel method based on feature extraction and reduction for the detection of breast cancer in mammography images. First, we extract features from multiple pre-trained convolutional neural network (CNN) models, and then concatenate them. The most informative features are selected based on their mutual information with the target variable. Subsequently, the selected features can be classified using a machine learning algorithm. We evaluate our approach using four different machine learning algorithms: neural network (NN), k-nearest neighbor (kNN), random forest (RF), and support vector machine (SVM). Our results demonstrate that the NN-based classifier achieves an impressive accuracy of 92% on the RSNA dataset. This dataset is newly introduced and includes two views as well as additional features like age, which contributed to the improved performance. We compare our proposed algorithm with state-of-the-art methods and demonstrate its superiority, particularly in terms of accuracy and sensitivity. For the MIAS dataset, we achieve an accuracy as high as 94.5%, and for the DDSM dataset, an accuracy of 96% is attained. These results highlight the effectiveness of our method in accurately diagnosing breast lesions and surpassing existing approaches.

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Section: Introductionmentioning

confidence: 99%

Breast Cancer Detection in Mammography Images: A CNN-Based Approach with Feature Selection

Jafari

Karami

2023

Information

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“…For example, Spectrum is such a method with a self-tuning density-aware kernel and similarity network fusion (SNF) that transforms data views to sample networks and fuses them nonlinearly ( Wang et al, 2014 ; John et al, 2019 ). MRGC learns a robust graph for each data view and unifies them afterward ( Shi et al, 2023 ). However, all the above-mentioned methods either reform the feature space or compute the between-sample interactions from features, which becomes deficient with data containing much information in the between-feature interactions.…”

Section: Introductionmentioning

confidence: 99%

netMUG: a novel network-guided multi-view clustering workflow for dissecting genetic and facial heterogeneity

Li,

Melograna,

Hoskens

et al. 2023

Front. Genet.

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Introduction: Multi-view data offer advantages over single-view data for characterizing individuals, which is crucial in precision medicine toward personalized prevention, diagnosis, or treatment follow-up.Methods: Here, we develop a network-guided multi-view clustering framework named netMUG to identify actionable subgroups of individuals. This pipeline first adopts sparse multiple canonical correlation analysis to select multi-view features possibly informed by extraneous data, which are then used to construct individual-specific networks (ISNs). Finally, the individual subtypes are automatically derived by hierarchical clustering on these network representations.Results: We applied netMUG to a dataset containing genomic data and facial images to obtain BMI-informed multi-view strata and showed how it could be used for a refined obesity characterization. Benchmark analysis of netMUG on synthetic data with known strata of individuals indicated its superior performance compared with both baseline and benchmark methods for multi-view clustering. The clustering derived from netMUG achieved an adjusted Rand index of 1 with respect to the synthesized true labels. In addition, the real-data analysis revealed subgroups strongly linked to BMI and genetic and facial determinants of these subgroups.Discussion: netMUG provides a powerful strategy, exploiting individual-specific networks to identify meaningful and actionable strata. Moreover, the implementation is easy to generalize to accommodate heterogeneous data sources or highlight data structures.

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“…For example, Spectrum is such a method with a selftuning density-aware kernel and similarity network fusion (SNF) that transforms data views to sample networks and fuses them nonlinearly (Wang et al, 2014;John et al, 2019). MRGC learns a robust graph for each data view and unifies them afterward (Shi et al, 2023). However, all the above-mentioned methods either reform the feature space or compute the between-sample interactions from features, which becomes deficient with data containing much information in the between-feature interactions.…”

Section: Introductionmentioning

confidence: 99%

netMUG: a novel network-guided multi-view clustering workflow for dissecting genetic and facial heterogeneity

Melograna

Hoskens

et al. 2023

Preprint

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Multi-view data offer advantages over single-view data for characterizing individuals, which is crucial in precision medicine toward personalized prevention, diagnosis, or treatment follow-up. Here, we develop a network-guided multi-view clustering framework named netMUG to identify actionable subgroups of individuals. This pipeline first adopts sparse multiple canonical correlation analysis to select multi-view features possibly informed by extraneous data, which are then used to construct individual-specific networks (ISNs). Finally, the individual subtypes are automatically derived by hierarchical clustering on these network representations. We applied netMUG to a dataset containing genomic data and facial images to obtain BMI-informed multi-view strata and showed how it could be used for a refined obesity characterization. Benchmark analysis of netMUG on synthetic data with known strata of individuals indicated its superior performance compared with both baseline and benchmark methods for multi-view clustering. In addition, the real-data analysis revealed subgroups strongly linked to BMI and genetic and facial determinants of these classes. NetMUG provides a powerful strategy, exploiting individual-specific networks to identify meaningful and actionable strata. Moreover, the implementation is easy to generalize to accommodate heterogeneous data sources or highlight data structures.

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Multiview Robust Graph-Based Clustering for Cancer Subtype Identification

Cited by 9 publications

References 44 publications

Breast Cancer Detection in Mammography Images: A CNN-Based Approach with Feature Selection

Breast Cancer Detection in Mammography Images: A CNN-Based Approach with Feature Selection

netMUG: a novel network-guided multi-view clustering workflow for dissecting genetic and facial heterogeneity

netMUG: a novel network-guided multi-view clustering workflow for dissecting genetic and facial heterogeneity

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