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 using Chest Ct Images

2003 ◽  
Vol 44 (3) ◽  
pp. 252-257 ◽  
Author(s):  
D.-Y. Kim ◽  
J.-H. Kim ◽  
S.-M. Noh ◽  
J.-W. Park
Keyword(s):  
Pulmonary Nodule ◽  
Ct Images ◽  
Chest Ct ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection

scholarly journals Light Deep Model for Pulmonary Nodule Detection from CT Scan Images for Mobile Devices

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Mehedi Masud ◽  
Ghulam Muhammad ◽  
M. Shamim Hossain ◽  
Hesham Alhumyani ◽  
Sultan S. Alshamrani ◽  
...  
Keyword(s):  
Pulmonary Nodule ◽  
Data Analytics ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Big Data Analytics ◽  
Pulmonary Nodules ◽  
Cognitive Computing ◽  
Deep Model ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection

The emergence of cognitive computing and big data analytics revolutionize the healthcare domain, more specifically in detecting cancer. Lung cancer is one of the major reasons for death worldwide. The pulmonary nodules in the lung can be cancerous after development. Early detection of the pulmonary nodules can lead to early treatment and a significant reduction of death. In this paper, we proposed an end-to-end convolutional neural network- (CNN-) based automatic pulmonary nodule detection and classification system. The proposed CNN architecture has only four convolutional layers and is, therefore, light in nature. Each convolutional layer consists of two consecutive convolutional blocks, a connector convolutional block, nonlinear activation functions after each block, and a pooling block. The experiments are carried out using the Lung Image Database Consortium (LIDC) database. From the LIDC database, 1279 sample images are selected of which 569 are noncancerous, 278 are benign, and the rest are malignant. The proposed system achieved 97.9% accuracy. Compared to other famous CNN architecture, the proposed architecture has much lesser flops and parameters and is thereby suitable for real-time medical image analysis.

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.

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