Large residual multiple view 3D CNN for false positive reduction in pulmonary nodule detection

Dobrenkii, Anton; Kuleev, Ramil; Khan, Adil; Rivera, Adín Ramírez; Khattak, Asad Masood

doi:10.1109/cibcb.2017.8058549

Cited by 24 publications

(11 citation statements)

References 10 publications

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“…Data presented in Table 2 showcase the 22 studies that applied DL algorithms [6,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47]. Some of the authors tested different types of algorithms; the results shown in Table 2 are the best performing algorithms presented in the literature.…”

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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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%

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

2019

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“…Compared with existing methods that use 2D CNN with a complex structure or 2D CNN with extra three dimensional features [ 9 ], our 3D DCNN method can effectively capture and extract 3D features of lung nodules without using additional features. Moreover, our method greatly outperforms the state-of-the-art methods using 3D CNN [ 22 – 24 ]. They use shallow 3D CNNs while our method uses 3D DCNNs.…”

Section: Experiments and Resultsmentioning

confidence: 97%

“…To overcome the limitations of the methods that use 2D CNN, which cannot solve the fundamental problem, methods using 3D CNN have recently been proposed. A method using a shallow 3D CNN that can receive a 3D patch as an input was proposed [ 22 ]. The authors used three 3D CNNs with different input sizes.…”

Section: Introductionmentioning

confidence: 99%

Classification of lung nodules in CT scans using three-dimensional deep convolutional neural networks with a checkpoint ensemble method

et al. 2018

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BackgroundAccurately detecting and examining lung nodules early is key in diagnosing lung cancers and thus one of the best ways to prevent lung cancer deaths. Radiologists spend countless hours detecting small spherical-shaped nodules in computed tomography (CT) images. In addition, even after detecting nodule candidates, a considerable amount of effort and time is required for them to determine whether they are real nodules. The aim of this paper is to introduce a high performance nodule classification method that uses three dimensional deep convolutional neural networks (DCNNs) and an ensemble method to distinguish nodules between non-nodules.MethodsIn this paper, we use a three dimensional deep convolutional neural network (3D DCNN) with shortcut connections and a 3D DCNN with dense connections for lung nodule classification. The shortcut connections and dense connections successfully alleviate the gradient vanishing problem by allowing the gradient to pass quickly and directly. Connections help deep structured networks to obtain general as well as distinctive features of lung nodules. Moreover, we increased the dimension of DCNNs from two to three to capture 3D features. Compared with shallow 3D CNNs used in previous studies, deep 3D CNNs more effectively capture the features of spherical-shaped nodules. In addition, we use an alternative ensemble method called the checkpoint ensemble method to boost performance.ResultsThe performance of our nodule classification method is compared with that of the state-of-the-art methods which were used in the LUng Nodule Analysis 2016 Challenge. Our method achieves higher competition performance metric (CPM) scores than the state-of-the-art methods using deep learning. In the experimental setup ESB-ALL, the 3D DCNN with shortcut connections and the 3D DCNN with dense connections using the checkpoint ensemble method achieved the highest CPM score of 0.910.ConclusionThe result demonstrates that our method of using a 3D DCNN with shortcut connections, a 3D DCNN with dense connections, and the checkpoint ensemble method is effective for capturing 3D features of nodules and distinguishing nodules between non-nodules.

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“…Recently, 3D CNNs have shown promise in 3D shape recognition, 3D object detection, CAD classification, hand gesture recognition, and diagnostic imaging predictions . 3D CNNs can reach into 3D space and aggregate authentically three‐dimensional information about a structure.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, 3D CNNs have shown promise in 3D shape recognition, 3D object detection, CAD classification, hand gesture recognition, and diagnostic imaging predictions. 16,[25][26][27][28][29] 3D CNNs can reach into 3D space and aggregate authentically three-dimensional information about a structure. 3D CNNs have the ability to train on a richer and more holistic learning environment, but are much more memory-intensive than 2D models.…”

Section: Introductionmentioning

confidence: 99%

A convolutional neural network algorithm for automatic segmentation of head and neck organs at risk using deep lifelong learning

et al. 2019

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Purpose This study suggests a lifelong learning‐based convolutional neural network (LL‐CNN) algorithm as a superior alternative to single‐task learning approaches for automatic segmentation of head and neck (OARs) organs at risk. Methods and materials Lifelong learning‐based convolutional neural network was trained on twelve head and neck OARs simultaneously using a multitask learning framework. Once the weights of the shared network were established, the final multitask convolutional layer was replaced by a single‐task convolutional layer. The single‐task transfer learning network was trained on each OAR separately with early stoppage. The accuracy of LL‐CNN was assessed based on Dice score and root‐mean‐square error (RMSE) compared to manually delineated contours set as the gold standard. LL‐CNN was compared with 2D‐UNet, 3D‐UNet, a single‐task CNN (ST‐CNN), and a pure multitask CNN (MT‐CNN). Training, validation, and testing followed Kaggle competition rules, where 160 patients were used for training, 20 were used for internal validation, and 20 in a separate test set were used to report final prediction accuracies. Results On average contours generated with LL‐CNN had higher Dice coefficients and lower RMSE than 2D‐UNet, 3D‐Unet, ST‐ CNN, and MT‐CNN. LL‐CNN required ~72 hrs to train using a distributed learning framework on 2 Nvidia 1080Ti graphics processing units. LL‐CNN required 20 s to predict all 12 OARs, which was approximately as fast as the fastest alternative methods with the exception of MT‐CNN. Conclusions This study demonstrated that for head and neck organs at risk, LL‐CNN achieves a prediction accuracy superior to all alternative algorithms.

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Large residual multiple view 3D CNN for false positive reduction in pulmonary nodule detection

Cited by 24 publications

References 10 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

Classification of lung nodules in CT scans using three-dimensional deep convolutional neural networks with a checkpoint ensemble method

A convolutional neural network algorithm for automatic segmentation of head and neck organs at risk using deep lifelong learning

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