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DeepLNAnno: a Web-Based Lung Nodules Annotating System for CT Images.

Lung cancer is one of the most common and fatal types of cancer, and pulmonary nodule detection plays a crucial role in the screening and diagnosis of this disease. A well-trained deep neural network model can help doctors to find nodules on computed tomography(CT) images while requiring lots of lab...

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Published in:Journal of Medical Systems Vol. 43; no. 7; pp. 1 - 10
Main Authors: Chen, Sihang, Guo, Jixiang, Wang, Chengdi, Xu, Xiuyuan, Yi, Zhang, Li, Weimin
Format: diagnostic images tables/charts Journal Article
Published: Springer Nature Jul2019
Online Access:View this record in EBSCOhost
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      dt: Jul2019
      vid: 43
      iid: 7
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10916-019-1258-9
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        atl: DeepLNAnno: a Web-Based Lung Nodules Annotating System for CT Images.
      aug:
        au:
          Chen, Sihang
          Guo, Jixiang
          Wang, Chengdi
          Xu, Xiuyuan
          Yi, Zhang
          Li, Weimin
        affil: Machine Intelligence Laboratory, College of Computer Science, Sichuan University, 610065, Chengdu, People's Republic of China
      sug:
        subj:
          Neural Networks (Computer) Methods
          Radiographic Image Interpretation, Computer-Assisted Methods
          Tomography, X-Ray Computed
          Solitary Pulmonary Nodule Diagnosis
          Technology
          Imaging, Three-Dimensional
          Diffusion of Innovation
          Lung Neoplasms
          Cancer Screening
      ab: Lung cancer is one of the most common and fatal types of cancer, and pulmonary nodule detection plays a crucial role in the screening and diagnosis of this disease. A well-trained deep neural network model can help doctors to find nodules on computed tomography(CT) images while requiring lots of labeled data. However, currently available annotating systems are not suitable for annotating pulmonary nodules in CT images. We propose a web-based lung nodules annotating system named as DeepLNAnno. DeepLNAnno has a unique three-tier working process and loads of features like semi-automatic annotation, which not only make it much easier for doctors to annotate compared to some other annotating systems but also increase the accuracy of the labels. We invited a medical group from West China Hospital to annotate the CT images using our DeepLNAnno system, and collected a large number of labeled data. The results of our experiments demonstrated that a usable nodule-detection system is developed, and good benchmark scores on our evaluation data are obtained.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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