Transferable Multi-model Ensemble for Benign-Malignant Lung Nodule Classification on Chest CT

Xie, Yutong; Xia, Yong; Zhang, Jianpeng; Feng, David Dagan; Fulham, Michael; Cai, Weidong

doi:10.1007/978-3-319-66179-7_75

Cited by 62 publications

(31 citation statements)

References 16 publications

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“…A preliminary version of this work was presented in MICCAI 2017 [40]. In this paper, we have substantially revised and extended the original paper.…”

Section: Introductionmentioning

confidence: 99%

Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT

Xie

Xia

Zhang

et al. 2019

IEEE Trans. Med. Imaging

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395

170

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The accurate identification of malignant lung nodules on chest CT is critical for the early detection of lung cancer, which also offers patients the best chance of cure. Deep learning methods have recently been successfully introduced to computer vision problems, although substantial challenges remain in the detection of malignant nodules due to the lack of large training datasets. In this paper, we propose a multi-view knowledge-based collaborative (MV-KBC) deep model to separate malignant from benign nodules using limited chest CT data. Our model learns 3D lung nodule characteristics by decomposing a 3D nodule into nine fixed views. For each view, we construct a knowledge-based collaborative (KBC) submodel, where three types of image patches are designed to fine-tune three pre-trained ResNet-50 networks that characterize the nodules' overall appearance, voxel and shape heterogeneity, respectively. We jointly use the nine KBC submodels to classify lung nodules with an adaptive weighting scheme learned during the error back propagation, which enables the MV-KBC model to be trained in an end-to-end manner. The penalty loss function is used for better reduction of the false negative rate with a minimal effect on the overall performance of the MV-KBC model. We tested our method on the benchmark LIDC-IDRI dataset and compared it to five state-of-the-art classification approaches. Our results show that the MV-KBC model achieved an accuracy of 91.60% for lung nodule classification with an AUC of 95.70%. These results are markedly superior to the state-of-the-art approaches.

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“…A preliminary version of this work was presented in MICCAI 2017 [40]. In this paper, we have substantially revised and extended the original paper.…”

Section: Introductionmentioning

confidence: 99%

Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT

Xie

Xia

Zhang

et al. 2019

IEEE Trans. Med. Imaging

Self Cite

395

170

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“…In a recent study, Sahu et al 13 proposed a lightweight multi‐section CNN for lung nodule classification and achieved a good result in reducing memory usage. Compared with the work 14 proposed by Xie et al, it reduced memory usage by 80% and maintained an accuracy rate of 93.18%. But the phenomenon of high memory occupation still exists.…”

Section: Related Workmentioning

confidence: 82%

“…It is found that Anand et al 42 achieved the lowest accuracy in this table, because this method only use coupled textural feature to predict lung tumor. Xie et al 14 adopted transferable multi‐model for better mining features of lung nodules and the highest accuracy achieved. Our method, which combines deep visual features learned by CNN and relevant information obtained by LSTM, achieved good result using sensitivity metric while having limited data.…”

Section: Resultsmentioning

confidence: 99%

A serialized classification method for pulmonary nodules based on lightweight cascaded convolutional neural network‐long short‐term memory

Peng

2020

Int J Imaging Syst Tech

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Computer Assisted Diagnosis (CAD) is an effective method to detect lung cancer from computed tomography (CT) scans. The development of artificial neural network makes CAD more accurate in detecting pathological changes. Due to the complexity of the lung environment, the existing neural network training still requires large datasets, excessive time, and memory space. To meet the challenge, we analysis 3D volumes as serialized 2D slices and present a new neural network structure lightweight convolutional neural network (CNN)‐long short‐term memory (LSTM) for lung nodule classification. Our network contains two main components: (a) optimized lightweight CNN layers with tiny parameter space for extracting visual features of serialized 2D images, and (b) LSTM network for learning relevant information among 2D images. In all experiments, we compared the training results of several models and our model achieved an accuracy of 91.78% for lung nodule classification with an AUC of 93%. We used fewer samples and memory space to train the model, and we achieved faster convergence. Finally, we analyzed and discussed the feasibility of migrating this framework to mobile devices. The framework can also be applied to cope with the small amount of training data and the development of mobile health device in future.

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“…Zhao et al [76] proposed an agile CNN model to overcome the challenges of small-scale medical datasets and nodules. Considering the limited chest CT data, Xie et al [77] used transfer learning algorithm to separate benign and malignant pulmonary nodules. Shen et al [78] presented a multicrop CNN (MC-CNN) to automatically extract nodule salient information for the investigation of the lung nodule malignancy suspiciousness.…”

Section: Pulmonary Nodule Classificationmentioning

confidence: 99%

Survey on deep learning for pulmonary medical imaging

Song

Tian

et al. 2019

Front. Med.

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As a promising method in artificial intelligence, deep learning has been proven successful in several domains ranging from acoustics and images to natural language processing. With medical imaging becoming an important part of disease screening and diagnosis, deep learning-based approaches have emerged as powerful techniques in medical image areas. In this process, feature representations are learned directly and automatically from data, leading to remarkable breakthroughs in the medical field. Deep learning has been widely applied in medical imaging for improved image analysis. This paper reviews the major deep learning techniques in this time of rapid evolution and summarizes some of its key contributions and state-of-the-art outcomes. The topics include classification, detection, and segmentation tasks on medical image analysis with respect to pulmonary medical images, datasets, and benchmarks. A comprehensive overview of these methods implemented on various lung diseases consisting of pulmonary nodule diseases, pulmonary embolism, pneumonia, and interstitial lung disease is also provided. Lastly, the application of deep learning techniques to the medical image and an analysis of their future challenges and potential directions are discussed.

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Transferable Multi-model Ensemble for Benign-Malignant Lung Nodule Classification on Chest CT

Cited by 62 publications

References 16 publications

Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT

Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT

A serialized classification method for pulmonary nodules based on lightweight cascaded convolutional neural network‐long short‐term memory

Survey on deep learning for pulmonary medical imaging

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