Learning Lung Nodule Malignancy Likelihood from Radiologist Annotations or Diagnosis Data

Gonçalves, Luis F.F.F.; Novo, Jorge; Cunha, António; Campilho, Aurélio

doi:10.1007/s40846-017-0317-2

Cited by 21 publications

(8 citation statements)

References 49 publications

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“…Eight out of 19 studies proposed an algorithm applying a type of Support-vector machine (SVM) classifier [7,8,10,12,13,20,21,24]. These studies achieved some of the best results with regards to accuracy, sensitivity, specificity, and AUC, and all applied an SVM classifier and a type of feature extraction with focus on shape, intensity, or texture.…”

Section: Resultsmentioning

confidence: 99%

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Automatic Pulmonary Nodule Detection Applying Deep Learning or Machine Learning Algorithms to the LIDC-IDRI Database: A Systematic Review

2019

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The aim of this study was to provide an overview of the literature available on machine learning (ML) algorithms applied to the Lung Image Database Consortium Image Collection (LIDC-IDRI) database as a tool for the optimization of detecting lung nodules in thoracic CT scans. This systematic review was compiled according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only original research articles concerning algorithms applied to the LIDC-IDRI database were included. The initial search yielded 1972 publications after removing duplicates, and 41 of these articles were included in this study. The articles were divided into two subcategories describing their overall architecture. The majority of feature-based algorithms achieved an accuracy >90% compared to the deep learning (DL) algorithms that achieved an accuracy in the range of 82.2%–97.6%. In conclusion, ML and DL algorithms are able to detect lung nodules with a high level of accuracy, sensitivity, and specificity using ML, when applied to an annotated archive of CT scans of the lung. However, there is no consensus on the method applied to determine the efficiency of ML algorithms.

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Section: Resultsmentioning

confidence: 99%

“…The table displays 12 studies marked with a star [7,8,10,12,13,14,16,20,21,22,23,24]. These studies applied several alternative combinations of features, classifiers, or validation methods.…”

Section: Resultsmentioning

confidence: 99%

Automatic Pulmonary Nodule Detection Applying Deep Learning or Machine Learning Algorithms to the LIDC-IDRI Database: A Systematic Review

2019

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“…In the past years numerous works have addressed the problem of classifying the malignancy of pulmonary nodules in CT scans; some of these works use as features only radiologists annotations of the nodules and perform classification for example with rule-based [6] and statistical learning [10] methods or by building a machine learning classifier [7] or classifiers ensemble [11,12].…”

Section: Related Workmentioning

confidence: 99%

“…Conventional solutions (e.g. [6,7]) propose engineering handcrafted features extracted directly from the CT image to build standard machine learning classifiers. This approach achieves satisfactory results when nodule candidates are well-defined, but shows some shortcomings when the nodules present complex and different sizes, shapes and context.…”

Section: Introductionmentioning

confidence: 99%

Integration of convolutional neural networks for pulmonary nodule malignancy assessment in a lung cancer classification pipeline

Bonavita

Rafael-Palou

Ceresa

et al. 2020

Computer Methods and Programs in Biomedicine

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Background and Objective: The early identification of malignant pulmonary nodules is critical for a better lung cancer prognosis and a less invasive chemo or radio therapies. Nodule malignancy assessment done by radiologists is extremely useful for planning a preventive intervention but is, unfortunately, a complex, time-consuming and error-prone task. This explains the lack of large datasets containing radiologists malignancy characterization of nodules; Methods: In this article, we propose to assess nodule malignancy through 3D convolutional neural networks and to integrate it in an automated end-to-end existing pipeline of lung cancer detection. For training and testing purposes we used independent subsets of the LIDC dataset; Results: Adding the probabilities of nodules malignity in a baseline lung cancer pipeline improved its F1-weighted score by 14.7 %, whereas integrating the malignancy model itself using transfer learning outperformed the baseline prediction by 11.8 % of F1-weighted score;Conclusions: Despite the limited size of the lung cancer datasets, integrating predictive models of nodule malignancy improves prediction of lung cancer.

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“…Many machine-learning algorithms are applied to Lung CAD systems, which are mainly divided into two categories: one is the traditional machine-learning algorithms, such as random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN) algorithm; the other one is deep learning algorithms, among which convolutional neural networks (CNN) is the most widely used, such as VGG16 [5], U-Net [6], and ResNet [7]. Traditional machine-learning methods generally design texture features, morphological features, and other handcrafted features according to doctors' suggestions, and then input them into the appropriate classifier, of which SVM is the most commonly used [8][9][10][11]. SVM is a traditional machine-learning method, mainly applied to small sample data, with strong interpretability and deep theoretical foundation [12].…”

Section: Introductionmentioning

confidence: 99%

Research on Key Algorithms of the Lung CAD System Based on Cascade Feature and Hybrid Swarm Intelligence Optimization for MKL‐SVM

Chang

Zheng

2021

Computational Intelligence and Neuroscience

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Feature selection and lung nodule recognition are the core modules of the lung computer-aided detection (Lung CAD) system. To improve the performance of the Lung CAD system, algorithmic research is carried out for the above two parts, respectively. First, in view of the poor interpretability of deep features and the incomplete expression of clinically defined handcrafted features, a feature cascade method is proposed to obtain richer feature information of nodules as the final input of the classifier. Second, to better map the global characteristics of samples, the multiple kernel learning support vector machine (MKL-SVM) algorithm with a linear convex combination of polynomial kernel and sigmoid kernel is proposed. Furthermore, this paper applied the methods for speed contraction factor and roulette strategy, and a mixture of simulated annealing (SA) and particle swarm optimization (PSO) is used for global optimization, so as to solve the problem that the PSO is easy to lose particle diversity and fall into the local optimal solution as well as improve the model’s training speed. Therefore, the MKL-SVM algorithm is presented in this paper, which is based on swarm intelligence optimization is proposed for lung nodule recognition. Finally, the algorithm construction experiments are conducted on the cooperative hospital dataset and compared with 8 advanced algorithms on the public dataset LUNA16. The experimental results show that the proposed algorithms can improve the accuracy of lung nodule recognition and reduce the missed detection of nodules.

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Learning Lung Nodule Malignancy Likelihood from Radiologist Annotations or Diagnosis Data

Cited by 21 publications

References 49 publications

Automatic Pulmonary Nodule Detection Applying Deep Learning or Machine Learning Algorithms to the LIDC-IDRI Database: A Systematic Review

Automatic Pulmonary Nodule Detection Applying Deep Learning or Machine Learning Algorithms to the LIDC-IDRI Database: A Systematic Review

Integration of convolutional neural networks for pulmonary nodule malignancy assessment in a lung cancer classification pipeline

Research on Key Algorithms of the Lung CAD System Based on Cascade Feature and Hybrid Swarm Intelligence Optimization for MKL‐SVM

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