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COMPLEMENTARY FEATURES FOR RADIOMIC ANALYSIS OF MALIGNANT AND BENIGN MEDIASTINAL LYMPH NODES
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-4255-5130
2017 (English)In: 2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), IEEE , 2017, p. 3849-3853Conference paper, Published paper (Refereed)
Abstract [en]

The importance of developing effective strategies for investigating mediastinal lymph-node metastases in non-small cell lung cancers is increasingly emphasized. It is because the precise detection of this metastatic disease is critical for optimal surgical intervention and treatment for patients with lung cancer. Existing medical image analysis is of limited power for mediastinal lymph-node staging on computed tomography (CT). Motivated by the radiomics hypothesis, this paper explored deep-learning, texture features and their combinations to ascertain subtle difference between malignant and benign mediastinal lymph nodes on CT. The radiomics-based results are found to be promising for differentiating malignant from benign mediastinal lymph nodes of patients with lung cancer.

Place, publisher, year, edition, pages
IEEE , 2017. p. 3849-3853
Series
IEEE International Conference on Image Processing ICIP, ISSN 1522-4880
Keywords [en]
Lung metastasis; benign; computed tomography; radiomics; deep learning; texture
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-147823DOI: 10.1109/ICIP.2017.8297003ISI: 000428410703196ISBN: 978-1-5090-2175-8 (print)OAI: oai:DiVA.org:liu-147823DiVA, id: diva2:1205609
Conference
24th IEEE International Conference on Image Processing (ICIP)
Available from: 2018-05-14 Created: 2018-05-14 Last updated: 2018-05-23

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Pham, Tuan
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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf