Weak Segmentations and Ensemble Learning to Predict Semantic Ratings of Lung Nodules

Smith, Ethan; Stein, Patrick; Furst, Jacob; Raicu, Daniela

doi:10.1109/icmla.2013.170

Cited by 14 publications

(8 citation statements)

References 16 publications

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“…Figure 2 shows CT images with different ratings of semantic features. For the specific definitions of each category's ratings, refer to [2] and [12]. For example, the texture characteristic provides meaningful information regarding nodule appearance (''Non-Solid'', ''Part Solid/(Mixed)'', ''Solid'') while malignancy characteristic captures the likelihood of malignancy (''Highly Unlikely'', ''Moderately Unlikely'', ''Indeterminate'', ''Moderately Suspicious'', ''Highly Suspicious'') as perceived by the LIDC radiologists.…”

Section: Dataset and Proposed Approachesmentioning

confidence: 99%

“…It affects a wide range of the population, especially those with unhealthy habits. The semantic features of lung nodules in CT images provide the foundation for the early diagnosis and monitoring of the disease [1]- [2]. For instance, as illustrated in the diagnostic guidelines from several medical associations, high-level texture features, such as the nodule solidity and the semantic morphology feature of speculation, are crucial for the differentiation of pulmonary nodules.…”

Section: Introductionmentioning

confidence: 99%

“…Among the three types of features, the geometric features are mainly used on lesions having fixed geometrical properties. The other two types of features, especially the textural features, are used more often [2].…”

Section: Introductionmentioning

confidence: 99%

“…The experimental dataset and computational features, causal discovery based on streaming features algorithm, causal discovery with symmetrical uncertainty based on streaming features algorithm and support vector machines based on streaming features algorithm are illustrated in Section 3. In the experimental result and analysis section, we detail the selection of computational features and the setting of corresponding parameters and describe an experiment to demonstrate their effectiveness for semantic rating prediction in comparison with the ensemble learning (EL) algorithm [2] without feature selection. In addition, the existing feature selection algorithms are compared: L2,1-norm regularized discriminative feature selection for unsupervised learning algorithm (UDFS) [31], feature selection and kernel learning for local learning -based clustering algorithm (LLCFS) [29], Laplacian score for feature selection algorithm (LASSO) [30], and dependence guided unsupervised feature selection algorithm(GUFS) [28].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Semantic Features Prediction for Pulmonary Nodule Diagnosis Based on Online Streaming Feature Selection

Yang

Fang

et al. 2019

IEEE Access

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Early diagnosis significantly improves the survival rate in lung carcinoma patients. This study attempts to construct a predictive network between the computational features and semantic features of pulmonary nodules using online feature selection and causal structure learning. In this paper, we exploit the causal discovery based on the streaming feature algorithm and causal discovery with symmetrical uncertainty based on the streaming feature algorithm. Different from the traditional learning methods that usually obtain all computational features in advance and then select the optimal subset of features from the computational features, the proposed approach integrates online streaming feature selection with causal structure learning. The critical challenges in this integration include: 1) the dynamic selection of computational features and 2) how to evaluate the feature subsets and implement causal structure learning. In addition, considering that building a causal structure network is a time-consuming process, we improve the process by using support vector machines based on the streaming feature algorithm. The experimental results show that our proposed algorithms improve on other traditional feature selection algorithms and ensemble learning algorithm without feature selection with regard to learning accuracy.

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Section: Dataset and Proposed Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Semantic Features Prediction for Pulmonary Nodule Diagnosis Based on Online Streaming Feature Selection

Yang

Fang

et al. 2019

IEEE Access

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“…Content based image retrieval (CBIR) algorithms are also applied for lung nodule detection and classification [ 31 ]. In yet another interesting research [ 32 ] computer-derived weak segmentation algorithm is proposed which is later used in classification algorithms for cancerous nodule prediction.…”

Section: Introductionmentioning

confidence: 99%

Ant Colony Optimization Approaches to Clustering of Lung Nodules from CT Images

Gopalakrishnan¹,

Kuppusamy

2014

Computational and Mathematical Methods in Medicine

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Lung cancer is becoming a threat to mankind. Applying machine learning algorithms for detection and segmentation of irregular shaped lung nodules remains a remarkable milestone in CT scan image analysis research. In this paper, we apply ACO algorithm for lung nodule detection. We have compared the performance against three other algorithms, namely, Otsu algorithm, watershed algorithm, and global region based segmentation. In addition, we suggest a novel approach which involves variations of ACO, namely, refined ACO, logical ACO, and variant ACO. Variant ACO shows better reduction in false positives. In addition we propose black circular neighborhood approach to detect nodule centers from the edge detected image. Genetic algorithm based clustering is performed to cluster the nodules based on intensity, shape, and size. The performance of the overall approach is compared with hierarchical clustering to establish the improvisation in the proposed approach.

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A computed tomography signs quantization analysis method for pulmonary nodules malignancy grading

Chen

Xia

Duan

et al. 2021

Int J Imaging Syst Tech

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In order to improve the accuracy of pulmonary nodules malignancy grading, we propose a method to implement quantitative analysis for lung nodules using computed tomography (CT) signs. Firstly, we construct feature sets of CT signs by combing the radiomics features with the higher‐order features extracted from a convolutional neural network. Secondly, on the basis of the mixed feature set, an evolutionary ensemble learning mechanism is used to generate a classifier to get the quantitative scores for seven lung nodule CT signs. Finally, the scores of seven CT signs are input into a multiclassifier optimized by the differential evolution algorithm to acquire the grade of malignancy. In the experimental study, 2000 lung nodule samples from the LIDC‐IDRI dataset were used to train and test the evolutionary ensemble learner and malignancy classifier. The results show that the recognition accuracy of seven CT signs can reach more than 0.964. Comparison with many typical algorithms, the proposed method not only gets higher accuracy in pulmonary nodules malignancy grading but also can make the result more interpretable.

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Weak Segmentations and Ensemble Learning to Predict Semantic Ratings of Lung Nodules

Cited by 14 publications

References 16 publications

Semantic Features Prediction for Pulmonary Nodule Diagnosis Based on Online Streaming Feature Selection

Semantic Features Prediction for Pulmonary Nodule Diagnosis Based on Online Streaming Feature Selection

Ant Colony Optimization Approaches to Clustering of Lung Nodules from CT Images

A computed tomography signs quantization analysis method for pulmonary nodules malignancy grading

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