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Unsupervised Approach for Malignancy Assessment of Lung Nodules in Computed Tomography Scans Using Radiomic Features

AuthID
P-00X-G09

Author(s)

Teixeira, M

Tipo de Documento

Proceedings Paper

Year published

2022

Publicado

in 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC 2022, Glasgow, Scotland, United Kingdom, July 11-15, 2022 in EMBC, ISSN: 1557170X

Volume: 2022-July, Páginas: 2037-2040 (3)

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Publication Identifiers

DOI: 10.1109/embc48229.2022.9871704

DBLP: conf/embc/TeixeiraPSCO22

Pubmed: 36086366

SCOPUS: 2-s2.0-85138128553

Unpaywall: 10.1109/embc48229.2022.9871704

Source Identifiers

ISSN: 1557170X

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Unsupervised Approach for Malignancy Assessment of Lung Nodules in Computed Tomography Scans Using Radiomic Features

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Unsupervised Approach for Malignancy Assessment of Lung Nodules in Computed Tomography Scans Using Radiomic Features

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