scholarly journals Deep Learning in CT Images: Automated Pulmonary Nodule Detection for Subsequent Management Using Convolutional Neural Network

2020 ◽  
Vol Volume 12 ◽  
pp. 2979-2992 ◽  
Author(s):  
Yi-Ming Xu ◽  
Teng Zhang ◽  
Hai Xu ◽  
Liang Qi ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Pulmonary Nodule ◽  
Ct Images ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection

scholarly journals The Performance of Deep Learning Algorithms on Automatic Pulmonary Nodule Detection and Classification Tested on Different Datasets That Are Not Derived from LIDC-IDRI: A Systematic Review

Diagnostics ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 207 ◽  
Author(s):  
Dana Li ◽  
Bolette Mikela Vilmun ◽  
Jonathan Frederik Carlsen ◽  
Elisabeth Albrecht-Beste ◽  
Carsten Ammitzbøl Lauridsen ◽  
...  
Keyword(s):  
Neural Network ◽  
Systematic Review ◽  
Deep Learning ◽  
Pulmonary Nodule ◽  
Image Database ◽  
Ct Scans ◽  
Learning Technology ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection ◽  
And Training

The aim of this study was to systematically review the performance of deep learning technology in detecting and classifying pulmonary nodules on computed tomography (CT) scans that were not from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) database. Furthermore, we explored the difference in performance when the deep learning technology was applied to test datasets different from the training datasets. Only peer-reviewed, original research articles utilizing deep learning technology were included in this study, and only results from testing on datasets other than the LIDC-IDRI were included. We searched a total of six databases: EMBASE, PubMed, Cochrane Library, the Institute of Electrical and Electronics Engineers, Inc. (IEEE), Scopus, and Web of Science. This resulted in 1782 studies after duplicates were removed, and a total of 26 studies were included in this systematic review. Three studies explored the performance of pulmonary nodule detection only, 16 studies explored the performance of pulmonary nodule classification only, and 7 studies had reports of both pulmonary nodule detection and classification. Three different deep learning architectures were mentioned amongst the included studies: convolutional neural network (CNN), massive training artificial neural network (MTANN), and deep stacked denoising autoencoder extreme learning machine (SDAE-ELM). The studies reached a classification accuracy between 68–99.6% and a detection accuracy between 80.6–94%. Performance of deep learning technology in studies using different test and training datasets was comparable to studies using same type of test and training datasets. In conclusion, deep learning was able to achieve high levels of accuracy, sensitivity, and/or specificity in detecting and/or classifying nodules when applied to pulmonary CT scans not from the LIDC-IDRI database.

scholarly journals DeepEM: Deep 3D ConvNets With EM For Weakly Supervised Pulmonary Nodule Detection

2018 ◽  
Author(s):  
Wentao Zhu ◽  
Yeeleng S. Vang ◽  
Yufang Huang ◽  
Xiaohui Xie
Keyword(s):  
Deep Learning ◽  
Pulmonary Nodule ◽  
Medical Image ◽  
Partial Information ◽  
Ct Images ◽  
Neural Nets ◽  
Training Dataset ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection ◽  
Weakly Supervised

AbstractRecently deep learning has been witnessing widespread adoption in various medical image applications. However, training complex deep neural nets requires large-scale datasets labeled with ground truth, which are often unavailable in many medical image domains. For instance, to train a deep neural net to detect pulmonary nodules in lung computed tomography (CT) images, current practice is to manually label nodule locations and sizes in many CT images to construct a sufficiently large training dataset, which is costly and difficult to scale. On the other hand, electronic medical records (EMR) contain plenty of partial information on the content of each medical image. In this work, we explore how to tap this vast, but currently unexplored data source to improve pulmonary nodule detection. We propose DeepEM, a novel deep 3D ConvNet framework augmented with expectation-maximization (EM), to mine weakly supervised labels in EMRs for pulmonary nodule detection. Experimental results show that DeepEM can lead to 1.5% and 3.9% average improvement in free-response receiver operating characteristic (FROC) scores on LUNA16 and Tianchi datasets, respectively, demonstrating the utility of incomplete information in EMRs for improving deep learning algorithms.1

Pulmonary Nodule Detection Based on Three-Dimensional Multiscale Convolutional Neural Network with Channel and Spatial Attention

2021 ◽  
Vol 11 (6) ◽  
pp. 1551-1559
Author(s):  
Yudu Zhao ◽  
Jun Ma ◽  
Zhenwei Peng ◽  
Hao Xia ◽  
Honglin Wan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Spatial Attention ◽  
Pulmonary Nodule ◽  
Lung Nodule ◽  
Three Dimensional ◽  
Great Success ◽  
Position Information ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection

Early screening for pulmonary nodules is currently an important means for reducing lung cancer mortality. In recent years, three-dimensional convolutional neural networks have achieved great success in the field of pulmonary nodule detection. This paper proposes a pulmonary nodule detection method based on a threedimensional multiscale convolutional neural network with channel and spatial attention. First, a multiscale module is designed to extract the image features at different scales. Second, a channel and spatial attention module is designed to mine the correlation information between features from the perspective of space and channel. Then the extracted features are sent to a pyramid-like fusion mechanism, so that the features contain both deep semantic information and shallow position information, which is conducive to object positioning and bounding box regression. In general, the experiments on the LUng Nodule Analysis 2016 (LUNA16) dataset show that the average free-response receiver operating characteristic (FROC) score is 0.846. Compared with other current advanced methods, the method is competitive and effective.

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