An Adaptive Learning Model for Multiscale Texture Features in Polyp Classification via Computed Tomo...
An Adaptive Learning Model for Multiscale Texture Features in Polyp Classification via Computed Tomographic Colonography
About this item
Full title
Author / Creator
Publisher
Switzerland: MDPI AG
Journal title
Language
English
Formats
Publication information
Publisher
Switzerland: MDPI AG
Subjects
More information
Scope and Contents
Contents
Objective: As an effective lesion heterogeneity depiction, texture information extracted from computed tomography has become increasingly important in polyp classification. However, variation and redundancy among multiple texture descriptors render a challenging task of integrating them into a general characterization. Considering these two problem...
Alternative Titles
Full title
An Adaptive Learning Model for Multiscale Texture Features in Polyp Classification via Computed Tomographic Colonography
Authors, Artists and Contributors
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_ee2e93f290234b67b994a02bf8514574
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_ee2e93f290234b67b994a02bf8514574
Other Identifiers
ISSN
1424-8220
E-ISSN
1424-8220
DOI
10.3390/s22030907
