Literature DB >> 31119468

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

Sihang Chen1, Jixiang Guo1, Chengdi Wang2, Xiuyuan Xu1, Zhang Yi1, Weimin Li3.   

Abstract

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.

Entities:  

Keywords:  Annotating system; Medical application; Medical data collection; Neural network

Year:  2019        PMID: 31119468     DOI: 10.1007/s10916-019-1258-9

Source DB:  PubMed          Journal:  J Med Syst        ISSN: 0148-5598            Impact factor:   4.460


  4 in total

1.  DeepLN: A Multi-Task AI Tool to Predict the Imaging Characteristics, Malignancy and Pathological Subtypes in CT-Detected Pulmonary Nodules.

Authors:  Chengdi Wang; Jun Shao; Xiuyuan Xu; Le Yi; Gang Wang; Congchen Bai; Jixiang Guo; Yanqi He; Lei Zhang; Zhang Yi; Weimin Li
Journal:  Front Oncol       Date:  2022-05-11       Impact factor: 5.738

Review 2.  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.

Authors:  Dana Li; Bolette Mikela Vilmun; Jonathan Frederik Carlsen; Elisabeth Albrecht-Beste; Carsten Ammitzbøl Lauridsen; Michael Bachmann Nielsen; Kristoffer Lindskov Hansen
Journal:  Diagnostics (Basel)       Date:  2019-11-29

3.  DeepLN: an artificial intelligence-based automated system for lung cancer screening.

Authors:  Jixiang Guo; Chengdi Wang; Xiuyuan Xu; Jun Shao; Lan Yang; Yuncui Gan; Zhang Yi; Weimin Li
Journal:  Ann Transl Med       Date:  2020-09

Review 4.  Machine and cognitive intelligence for human health: systematic review.

Authors:  Xieling Chen; Gary Cheng; Fu Lee Wang; Xiaohui Tao; Haoran Xie; Lingling Xu
Journal:  Brain Inform       Date:  2022-02-12
  4 in total

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