Matches in SemOpenAlex for { <https://semopenalex.org/work/W2738993979> ?p ?o ?g. }
Showing items 1 to 94 of
94
with 100 items per page.
- W2738993979 abstract "Abstract In recent years, deep learning techniques have been applied to the diagnosis of pulmonary nodules. In order to improve the pulmonary nodule diagnostic performance effectively, we propose a novel pulmonary nodule diagnosis method using dual‐modal deep supervised autoencoder based on extreme learning machine for which discriminative features are automatically learnt from the input data. The network is fed with nodule images in pairs obtained from computed tomography and positron emission tomography respectively. For each pair image, the high‐level discriminative features of nodules in computed tomography and positron emission tomography are extracted from stacked supervised autoencoder layers. The outputs of the proposed architecture are combined using an ideal fusion method to get the final classification. In the experiments, 5‐fold cross‐validation method is used to validate the proposed method on 1,600 pulmonary nodule images and our method reaches high‐classification sensitivities of 91.75% at 1.58 false positives per scan. Meanwhile, compared with other deep learning diagnosis methods, our method achieves better discriminative results and is highly suited to be used for pulmonary nodule diagnosis." @default.
- W2738993979 created "2017-07-31" @default.
- W2738993979 creator A5003662421 @default.
- W2738993979 creator A5011723492 @default.
- W2738993979 creator A5056543646 @default.
- W2738993979 creator A5070372620 @default.
- W2738993979 creator A5080765061 @default.
- W2738993979 date "2017-07-18" @default.
- W2738993979 modified "2023-10-05" @default.
- W2738993979 title "Pulmonary nodule diagnosis using dual-modal supervised autoencoder based on extreme learning machine" @default.
- W2738993979 cites W1898227994 @default.
- W2738993979 cites W1964155876 @default.
- W2738993979 cites W1998808035 @default.
- W2738993979 cites W2008751568 @default.
- W2738993979 cites W2025768430 @default.
- W2738993979 cites W2031878488 @default.
- W2738993979 cites W2032298282 @default.
- W2738993979 cites W2071342347 @default.
- W2738993979 cites W2111072639 @default.
- W2738993979 cites W2136922672 @default.
- W2738993979 cites W2142653084 @default.
- W2738993979 cites W2143350951 @default.
- W2738993979 cites W2301541953 @default.
- W2738993979 cites W2322371438 @default.
- W2738993979 cites W2328052071 @default.
- W2738993979 cites W2331596309 @default.
- W2738993979 cites W2339974927 @default.
- W2738993979 cites W2403443505 @default.
- W2738993979 cites W2581772000 @default.
- W2738993979 cites W2586480914 @default.
- W2738993979 cites W4255030935 @default.
- W2738993979 doi "https://doi.org/10.1111/exsy.12224" @default.
- W2738993979 hasPublicationYear "2017" @default.
- W2738993979 type Work @default.
- W2738993979 sameAs 2738993979 @default.
- W2738993979 citedByCount "13" @default.
- W2738993979 countsByYear W27389939792018 @default.
- W2738993979 countsByYear W27389939792019 @default.
- W2738993979 countsByYear W27389939792020 @default.
- W2738993979 countsByYear W27389939792021 @default.
- W2738993979 countsByYear W27389939792022 @default.
- W2738993979 countsByYear W27389939792023 @default.
- W2738993979 crossrefType "journal-article" @default.
- W2738993979 hasAuthorship W2738993979A5003662421 @default.
- W2738993979 hasAuthorship W2738993979A5011723492 @default.
- W2738993979 hasAuthorship W2738993979A5056543646 @default.
- W2738993979 hasAuthorship W2738993979A5070372620 @default.
- W2738993979 hasAuthorship W2738993979A5080765061 @default.
- W2738993979 hasConcept C101738243 @default.
- W2738993979 hasConcept C108583219 @default.
- W2738993979 hasConcept C126838900 @default.
- W2738993979 hasConcept C151730666 @default.
- W2738993979 hasConcept C153180895 @default.
- W2738993979 hasConcept C154945302 @default.
- W2738993979 hasConcept C2775842073 @default.
- W2738993979 hasConcept C2776731575 @default.
- W2738993979 hasConcept C41008148 @default.
- W2738993979 hasConcept C64869954 @default.
- W2738993979 hasConcept C71924100 @default.
- W2738993979 hasConcept C86803240 @default.
- W2738993979 hasConcept C97931131 @default.
- W2738993979 hasConceptScore W2738993979C101738243 @default.
- W2738993979 hasConceptScore W2738993979C108583219 @default.
- W2738993979 hasConceptScore W2738993979C126838900 @default.
- W2738993979 hasConceptScore W2738993979C151730666 @default.
- W2738993979 hasConceptScore W2738993979C153180895 @default.
- W2738993979 hasConceptScore W2738993979C154945302 @default.
- W2738993979 hasConceptScore W2738993979C2775842073 @default.
- W2738993979 hasConceptScore W2738993979C2776731575 @default.
- W2738993979 hasConceptScore W2738993979C41008148 @default.
- W2738993979 hasConceptScore W2738993979C64869954 @default.
- W2738993979 hasConceptScore W2738993979C71924100 @default.
- W2738993979 hasConceptScore W2738993979C86803240 @default.
- W2738993979 hasConceptScore W2738993979C97931131 @default.
- W2738993979 hasFunder F4320321001 @default.
- W2738993979 hasIssue "6" @default.
- W2738993979 hasLocation W27389939791 @default.
- W2738993979 hasOpenAccess W2738993979 @default.
- W2738993979 hasPrimaryLocation W27389939791 @default.
- W2738993979 hasRelatedWork W2024160000 @default.
- W2738993979 hasRelatedWork W2061273563 @default.
- W2738993979 hasRelatedWork W2669956259 @default.
- W2738993979 hasRelatedWork W2729514902 @default.
- W2738993979 hasRelatedWork W2773500201 @default.
- W2738993979 hasRelatedWork W2897995864 @default.
- W2738993979 hasRelatedWork W2939353110 @default.
- W2738993979 hasRelatedWork W2998168123 @default.
- W2738993979 hasRelatedWork W4287995534 @default.
- W2738993979 hasRelatedWork W4327774331 @default.
- W2738993979 hasVolume "34" @default.
- W2738993979 isParatext "false" @default.
- W2738993979 isRetracted "false" @default.
- W2738993979 magId "2738993979" @default.
- W2738993979 workType "article" @default.