Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans

Ginneken, Bram van; Setio, Arnaud Arindra Adiyoso; Jacobs, Colin; Ciompi, Francesco

doi:10.1109/isbi.2015.7163869

Cited by 210 publications

(158 citation statements)

References 8 publications

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“…Unlike that work, rather than copy the weights of the original pretrained CNN to the target CNN with additional layers, we use the pretrained CNN to project data into a new feature space through the propagation of the colonic polyp database into the CNN getting the resultant vector from the last CNNs layer, obtaining a new representation for each input sample. Subsequently, we use the feature vector set to train a linear classifier (e.g., support vector machines) in this representation to evaluate the results as used in [25, 26]. …”

Section: Methodsmentioning

confidence: 99%

“…Examples include the identification and pathology of X-ray and computer tomography modalities [25], automatic classification of pulmonary perifissural nodules [41], pulmonary nodule detection [26], and mammography mass lesion classification [42]. Moreover, in [26], Van Ginneken et al show that the combination of CNNs features and classical features for pulmonary nodule detection can improve the performance of the model.…”

Section: Methodsmentioning

confidence: 99%

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Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification

Ribeiro

Uhl

Wimmer

et al. 2016

Computational and Mathematical Methods in Medicine

143

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Recently, Deep Learning, especially through Convolutional Neural Networks (CNNs) has been widely used to enable the extraction of highly representative features. This is done among the network layers by filtering, selecting, and using these features in the last fully connected layers for pattern classification. However, CNN training for automated endoscopic image classification still provides a challenge due to the lack of large and publicly available annotated databases. In this work we explore Deep Learning for the automated classification of colonic polyps using different configurations for training CNNs from scratch (or full training) and distinct architectures of pretrained CNNs tested on 8-HD-endoscopic image databases acquired using different modalities. We compare our results with some commonly used features for colonic polyp classification and the good results suggest that features learned by CNNs trained from scratch and the “off-the-shelf” CNNs features can be highly relevant for automated classification of colonic polyps. Moreover, we also show that the combination of classical features and “off-the-shelf” CNNs features can be a good approach to further improve the results.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification

Ribeiro

Uhl

Wimmer

et al. 2016

Computational and Mathematical Methods in Medicine

143

View full text Add to dashboard Cite

show abstract

“…For example, Kumar et al 4 use an autoencoder (an unsupervised learning network) to extract useful features from annotated nodules, these features are used to learn to classify nodules as being malignant or benign. Next, Ginneken et al 5 have shown promising results using an off-the-shelf convolutional neural network (CNN), one that is pre-trained for an image recognition task. They use the network to obtain features which are used for classification.…”

Section: Introductionmentioning

confidence: 99%

Lung nodule detection using 3D convolutional neural networks trained on weakly labeled data

et al. 2016

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Early detection of lung nodules is currently the one of the most effective ways to predict and treat lung cancer. As a result, the past decade has seen a lot of focus on computer aided diagnosis (CAD) of lung nodules, whose goal is to efficiently detect, segment lung nodules and classify them as being benign or malignant. Effective detection of such nodules remains a challenge due to their arbitrariness in shape, size and texture. In this paper, we propose to employ 3D convolutional neural networks (CNN) to learn highly discriminative features for nodule detection in lieu of hand-engineered ones such as geometric shape or texture. While 3D CNNs are promising tools to model the spatio-temporal statistics of data, they are limited by their need for detailed 3D labels, which can be prohibitively expensive when compared obtaining 2D labels. Existing CAD methods rely on obtaining detailed labels for lung nodules, to train models, which is also unrealistic and time consuming. To alleviate this challenge, we propose a solution wherein the expert needs to provide only a point label, i.e., the central pixel of of the nodule, and its largest expected size. We use unsupervised segmentation to grow out a 3D region, which is used to train the CNN. Using experiments on the SPIE-LUNGx dataset, we show that the network trained using these weak labels can produce reasonably low false positive rates with a high sensitivity, even in the absence of accurate 3D labels.

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“…The most used incarnation of deep neural networks are convolutional networks161819, a supervised learning algorithm particularly suited to solve problems of classification of natural images192021, which has recently been applied to some applications in chest CT analysis615222324.…”

mentioning

confidence: 99%

Towards automatic pulmonary nodule management in lung cancer screening with deep learning

Ciompi

Chung

Riel

et al. 2017

Sci Rep

Self Cite

290

203

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The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy. According to the current guidelines, the workup of screen-detected nodules strongly relies on nodule size and nodule type. In this paper, we present a deep learning system based on multi-stream multi-scale convolutional networks, which automatically classifies all nodule types relevant for nodule workup. The system processes raw CT data containing a nodule without the need for any additional information such as nodule segmentation or nodule size and learns a representation of 3D data by analyzing an arbitrary number of 2D views of a given nodule. The deep learning system was trained with data from the Italian MILD screening trial and validated on an independent set of data from the Danish DLCST screening trial. We analyze the advantage of processing nodules at multiple scales with a multi-stream convolutional network architecture, and we show that the proposed deep learning system achieves performance at classifying nodule type that surpasses the one of classical machine learning approaches and is within the inter-observer variability among four experienced human observers.

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Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans

Cited by 210 publications

References 8 publications

Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification

Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification

Lung nodule detection using 3D convolutional neural networks trained on weakly labeled data

Towards automatic pulmonary nodule management in lung cancer screening with deep learning

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