Adaptive Weight Optimization for Classification of Imbalanced Data

Huang, Wenhao; Song, Guojie; Li, Man; Hu, Weisong; Xie, Kun

doi:10.1007/978-3-642-42057-3_69

Cited by 19 publications

(13 citation statements)

References 11 publications

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“…Thus, an adaptive weighting approach may help finding a group of optimal weights for the classes and achieving better performance. 38 Besides, the approaches of resampling the dataset, for example, undersampling of the majority classes or oversampling of the minority classes, 39,40 can be used to cope with class imbalance problem.…”

Section: Discussionmentioning

confidence: 99%

“…Nevertheless, the use of the inverse weighting from the dataset is not suited for every classifier, such as the RF classifier, where a large portion of the error‐free cases were still misclassified as the cases of MU errors. Thus, an adaptive weighting approach may help finding a group of optimal weights for the classes and achieving better performance 38 . Besides, the approaches of resampling the dataset, for example, undersampling of the majority classes or oversampling of the minority classes, 39,40 can be used to cope with class imbalance problem.…”

Section: Discussionmentioning

confidence: 99%

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The structural similarity index for IMRT quality assurance: radiomics‐based error classification

Wang

Zhou

et al. 2020

Medical Physics

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Purpose The implementation of radiomics and machine learning (ML) techniques on analyzing two‐dimensional gamma maps has been demonstrated superior to the conventional gamma analysis for error identification in intensity modulated radiotherapy (IMRT) quality assurance (QA). Recently, the Structural SIMilarity (SSIM) sub‐index maps were shown to be able to reveal the error types of the dose distributions. In this study, we aimed to apply radiomics analysis on SSIM sub‐index maps and develop ML models to classify delivery errors in patient‐specific dynamic IMRT QA. Methods Twenty‐one sliding‐window IMRT plans of 180 beams for three treatment sites were involved in this study. Four types of machine‐related errors of various magnitudes were simulated for each beam at each control point, including the monitor unit (MU) variations, same‐directional and opposite‐directional shifts of the multileaf collimators (MLCs) and random mispositioning of the MLCs. In the QA process, a total of 1620 portal dose (PD) images were acquired for the beams with and without errors. The predicted PD images of the original beams were set as references. To quantify the agreement between a measured PD image and the corresponding predicted PD image, four difference maps including three SSIM sub‐index maps, and one dose difference‐derived map were calculated. Then, radiomic features were extracted from the four difference maps of each measured PD image. We tested four typical classifiers including linear discriminant classifier (LDC), two supporting vector machine (SVM) classifiers, and random forest (RF) for this multiclass classification task. A nested cross‐validation scheme was used for model evaluations, where the SVM recursive feature elimination method was applied for feature selection. Finally, the performance of the ML model on identifying the error‐free and the erroneous cases was compared to that of the conventional gamma analysis. Results The statistics of the selected features showed that all of the difference maps and the feature categories made balanced contributions to solve this classification task. Best performance was achieved by the Linear‐SVM model with average overall classification accuracy of 0.86. Specifically, the average classification accuracies of the shift, opening, and the random errors were around 0.9. Moreover, ~80% of error‐free and MU errors were correctly classified. Using gamma analysis, the 3 mm/3% criterion was found insensitive to errors (sensitivity was only 0.33). Although the sensitivity to errors with the 2 mm/2% criterion increased to 0.79, still 8% worse than that of the ML model. Conclusions We proposed an ML‐based method for machine‐related error identification in patient‐specific dynamic IMRT QA, where radiomic analysis on SSIM sub‐index maps were used for feature extraction. With extensive validation to select the best features and classifiers, high accuracies in error classification were achieved. Compared with the conventional gamma threshold method, this approach has great potential in error...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The structural similarity index for IMRT quality assurance: radiomics‐based error classification

Wang

Zhou

et al. 2020

Medical Physics

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“…To provide a more balanced distribution of labels (successful CA strategies) in the dataset, we used two different approaches: (1) a stratified 5-fold cross validation which can be considered as a valid alternative to bootstrapping (Kohavi, 1995 ), and (2) weighting of the loss function based on the percentage of samples in each class, a common approach for unbalanced datasets (Thai-Nghe et al, 2010 ; Rosenberg, 2012 ; Huang et al, 2013 ). The addition of class weighting to the minimum percentage labelling method doesn't lead to a clear advantage ( Figure 9C ).…”

Section: Discussionmentioning

confidence: 99%

Toward Patient-Specific Prediction of Ablation Strategies for Atrial Fibrillation Using Deep Learning

et al. 2021

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Atrial fibrillation (AF) is a common cardiac arrhythmia that affects 1% of the population worldwide and is associated with high levels of morbidity and mortality. Catheter ablation (CA) has become one of the first line treatments for AF, but its success rates are suboptimal, especially in the case of persistent AF. Computational approaches have shown promise in predicting the CA strategy using simulations of atrial models, as well as applying deep learning to atrial images. We propose a novel approach that combines image-based computational modelling of the atria with deep learning classifiers trained on patient-specific atrial models, which can be used to assist in CA therapy selection. Therefore, we trained a deep convolutional neural network (CNN) using a combination of (i) 122 atrial tissue images obtained by unfolding patient LGE-MRI datasets, (ii) 157 additional synthetic images derived from the patient data to enhance the training dataset, and (iii) the outcomes of 558 CA simulations to terminate several AF scenarios in the corresponding image-based atrial models. Four CNN classifiers were trained on this patient-specific dataset balanced using several techniques to predict three common CA strategies from the patient atrial images: pulmonary vein isolation (PVI), rotor-based ablation (Rotor) and fibrosis-based ablation (Fibro). The training accuracy for these classifiers ranged from 96.22 to 97.69%, while the validation accuracy was from 78.68 to 86.50%. After training, the classifiers were applied to predict CA strategies for an unseen holdout test set of atrial images, and the results were compared to outcomes of the respective image-based simulations. The highest success rate was observed in the correct prediction of the Rotor and Fibro strategies (100%), whereas the PVI class was predicted in 33.33% of the cases. In conclusion, this study provides a proof-of-concept that deep neural networks can learn from patient-specific MRI datasets and image-derived models of AF, providing a novel technology to assist in tailoring CA therapy to a patient.

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“…The proposed model uses minimal data to train the CNN.Yung-Hui Li et al [11] suggested a CAD framework for DR based on fundus photos using deep CNN imagery in 2020.In 2012 Man Li et.al. [12] Proposed a common approach to handle imbalance dataset training bias issue in which technique is adopted to weighting samples in rare classes with high cost and then apply cost-sensitive learning strategies to fix the class imbalance issue.…”

Section: Introductionmentioning

confidence: 99%

Predicting Severity of Diabetic Retinopathy using Deep Learning Models

Shivsharan¹,

Ganorkar²

2021

IRJASH

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This paper presents deep learning models for the classification of Diabetic Retinopathy (DR) grades. The goal of this research is to find and create a deep learning model that will help us identify the image with high accuracy into one of the five phases of the DR as no DR, mild, moderate, severe, and proliferative DR.The whole work is developed using four steps. The first, using Ben Graham's pre-possessing form, the fundus images were pre-processed. Secondly, in order to train the models, the preprocessed images are contributed to the deep learning algorithm. The third,deep learning models such as Deep CNN, Dense Net, and Group 19 Visual Geometry (VGG19) are developed to predict the severity of the DR. The APTOS Blindness Detection dataset is used to train the proposed deep learning models. Since the data set is imbalanced in nature, the issue of training bias contributes to it. Therefore, at the time of training the models, class weight technique is used to eliminate the training bias problem. In the case of DR grading structures, the proposed deep learning models work well. The Dense Net has been found to work better than the other two models.

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Adaptive Weight Optimization for Classification of Imbalanced Data

Cited by 19 publications

References 11 publications

The structural similarity index for IMRT quality assurance: radiomics‐based error classification

The structural similarity index for IMRT quality assurance: radiomics‐based error classification

Toward Patient-Specific Prediction of Ablation Strategies for Atrial Fibrillation Using Deep Learning

Predicting Severity of Diabetic Retinopathy using Deep Learning Models

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