Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification

Ren, Liang-Rui; Gao, Ying-Lian; Liu, Jin-Xing; Shang, Junliang; Zheng, Chun-Hou

doi:10.1186/s12859-020-03790-1

Cited by 10 publications

(7 citation statements)

References 42 publications

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“…Besides these, there are also some classification models composed of hybrid methods. Ren et al [ 26 ] proposed an integrated method named correntropy-induced loss-based sparse robust graph regularized extreme learning machine and applied it to the classification and recognition of cancer samples. Gao et al [ 27 ] performed cancer classification based on SVM optimized by particle swarm optimization combined with artificial bee colony approaches, and the effectiveness of these methods was verified by the experimental results.…”

Section: Related Workmentioning

confidence: 99%

“…Gao et al [ 27 ] performed cancer classification based on SVM optimized by particle swarm optimization combined with artificial bee colony approaches, and the effectiveness of these methods was verified by the experimental results. In addition, with the continuous development of machine learning, many studies have shown that the application of ensemble learning to classification problems is often better than traditional classification algorithms and single classifiers, and it can also solve the problem of increased data volume and data diversification [ 23 , 26 ]. Therefore, a large number of classification models based on ensemble learning have been proposed.…”

Section: Related Workmentioning

confidence: 99%

“…Computational and Mathematical Methods in Medicine continuous development of machine learning, many studies have shown that the application of ensemble learning to classification problems is often better than traditional classification algorithms and single classifiers, and it can also solve the problem of increased data volume and data diversification [23,26]. Therefore, a large number of classification models based on ensemble learning have been proposed.…”

Section: Related Workmentioning

confidence: 99%

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Cancer Classification with a Cost-Sensitive Naive Bayes Stacking Ensemble

Xiong

2021

Computational and Mathematical Methods in Medicine

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Ensemble learning combines multiple learners to perform combinatorial learning, which has advantages of good flexibility and higher generalization performance. To achieve higher quality cancer classification, in this study, the fast correlation-based feature selection (FCBF) method was used to preprocess the data to eliminate irrelevant and redundant features. Then, the classification was carried out in the stacking ensemble learner. A library for support vector machine (LIBSVM), K -nearest neighbor (KNN), decision tree C4.5 (C4.5), and random forest (RF) were used as the primary learners of the stacking ensemble. Given the imbalanced characteristics of cancer gene expression data, the embedding cost-sensitive naive Bayes was used as the metalearner of the stacking ensemble, which was represented as CSNB stacking. The proposed CSNB stacking method was applied to nine cancer datasets to further verify the classification performance of the model. Compared with other classification methods, such as single classifier algorithms and ensemble algorithms, the experimental results showed the effectiveness and robustness of the proposed method in processing different types of cancer data. This method may therefore help guide cancer diagnosis and research.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Cancer Classification with a Cost-Sensitive Naive Bayes Stacking Ensemble

Xiong

2021

Computational and Mathematical Methods in Medicine

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“…4 . Correntropy-induced loss functions 21 , 22 were used to determine the weight ω, so as to improve the robustness.…”

Section: Related Work and Methodologymentioning

confidence: 99%

Focal Boundary Dice: Improved Breast Tumor Segmentation from MRI Scan

Yin¹,

Jian²,

Shen³

et al. 2023

J. Cancer

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Focal Boundary Dice, a new segmentation evaluation measure, was hereby presented, with the focus on boundary quality and class imbalance. Extensive analysis was carried out across different error types with varied object sizes of imaged tumors from Magnetic Resonance Imaging (MRI) scans, and the results show that Focal Boundary Dice is significantly more adaptive than the standard Focal and Dice measures to boundary errors for imaged tumors from MRI scans and does not over-penalize errors on the division of the boundary, including smaller imaged objects. Based on Boundary Dice, the standard evaluation protocols for tumor segmentation tasks were updated by proposing the Focal Boundary Dice. The contradiction between the target and the background area, and the conflict between the importance and the attention of boundary features were mainly solved. Meanwhile, a boundary attention module was introduced to further extract the tumor edge features. The new quality measure presents several desirable characteristics, including higher accuracy in the selection of hard samples, prediction/ground-truth pairs, and balanced responsiveness with across scales, which jointly make it more suitable for segmentation evaluation than other classification-focused measures such as combined Intersection-over-Union and Boundary binary cross-entropy loss, Boundary binary cross-entropy loss and Shape-aware Loss. The experiments show that the new evaluation metrics allow boundary quality improvements and image segmentation accuracy that are generally overlooked by current Dice-based evaluation metrics and deep learning models. It is expected that the adoption of the new boundary-adaptive evaluation metrics will facilitate the rapid progress in segmentation methods, and further contribute to the improvement of classification accuracy.

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“…In subfigure (b) of panel (2) of Figure 1, the zoomed-in window displays overlapping regions on chosen patches. To learn relevant features, we employ a sparse coding and dictionary learning method with an l1-regularized correntropy loss function [71][72][73][74] named Correntropy-induced Sparse-coding (CS), which is expected to improve the computational efficiency compared to Stochastic Coordinate Coding (SCC) [75]. Outliers may be removed with the use of the correntropy loss function [71][72][73][74], the impact of non-Gaussian noise on the data will be lessened by using the correntropy measure as a loss function.…”

Section: Surface Feature Dimensionality Reductionmentioning

confidence: 99%

Improved Prediction of Amyloid-β and Tau Burden Using Hippocampal Surface Multivariate Morphometry Statistics and Sparse Coding

Su²,

Zhu

et al. 2023

JAD

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Background: Amyloid-β (Aβ) plaques and tau protein tangles in the brain are the defining ‘A’ and ‘T’ hallmarks of Alzheimer’s disease (AD), and together with structural atrophy detectable on brain magnetic resonance imaging (MRI) scans as one of the neurodegenerative (‘N’) biomarkers comprise the “ATN framework” of AD. Current methods to detect Aβ/tau pathology include cerebrospinal fluid (invasive), positron emission tomography (PET; costly and not widely available), and blood-based biomarkers (promising but mainly still in development). Objective: To develop a non-invasive and widely available structural MRI-based framework to quantitatively predict the amyloid and tau measurements. Methods: With MRI-based hippocampal multivariate morphometry statistics (MMS) features, we apply our Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) method combined with the ridge regression model to individual amyloid/tau measure prediction. Results: We evaluate our framework on amyloid PET/MRI and tau PET/MRI datasets from the Alzheimer’s Disease Neuroimaging Initiative. Each subject has one pair consisting of a PET image and MRI scan, collected at about the same time. Experimental results suggest that amyloid/tau measurements predicted with our PASCP-MP representations are closer to the real values than the measures derived from other approaches, such as hippocampal surface area, volume, and shape morphometry features based on spherical harmonics. Conclusion: The MMS-based PASCP-MP is an efficient tool that can bridge hippocampal atrophy with amyloid and tau pathology and thus help assess disease burden, progression, and treatment effects.

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Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification

Cited by 10 publications

References 42 publications

Cancer Classification with a Cost-Sensitive Naive Bayes Stacking Ensemble

Cancer Classification with a Cost-Sensitive Naive Bayes Stacking Ensemble

Focal Boundary Dice: Improved Breast Tumor Segmentation from MRI Scan

Improved Prediction of Amyloid-β and Tau Burden Using Hippocampal Surface Multivariate Morphometry Statistics and Sparse Coding

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