Matches in SemOpenAlex for { <https://semopenalex.org/work/W3111291208> ?p ?o ?g. }
Showing items 1 to 93 of
93
with 100 items per page.
- W3111291208 endingPage "281" @default.
- W3111291208 startingPage "271" @default.
- W3111291208 abstract "Clinicians have shown an increasing interest in quantitative imaging for precision medicine. Imaging features can extract distinct phenotypic differences of tumours, potentially they can be used as a non-invasive prognostic tool and contribute for a better prediction of pathological Complete Response (pCR). However, the high-dimensional nature of the data brings many constraints, for which several approaches have been considered, with regularization techniques in the cutting-edge research front. In this work, classic lasso, ridge and the recently proposed priority-lasso are applied to high-dimensional imaging data, regarding a binary outcome. A breast cancer dataset, with radiomics, clinical and pathological information as features, was used. The application of sparsity techniques to the dataset enabled the selection of relevant features extracted in MRI of breast cancer patients, in order to identify the accuracy of those features and predict the pCR in the breast and the axilla." @default.
- W3111291208 created "2020-12-21" @default.
- W3111291208 creator A5023107971 @default.
- W3111291208 creator A5025206434 @default.
- W3111291208 creator A5025554206 @default.
- W3111291208 creator A5050926297 @default.
- W3111291208 creator A5056916227 @default.
- W3111291208 creator A5057794986 @default.
- W3111291208 creator A5064430711 @default.
- W3111291208 creator A5077039976 @default.
- W3111291208 creator A5080951501 @default.
- W3111291208 date "2020-01-01" @default.
- W3111291208 modified "2023-09-23" @default.
- W3111291208 title "Regularization Techniques in Radiomics: A Case Study on the Prediction of pCR in Breast Tumours and the Axilla" @default.
- W3111291208 cites W2106398669 @default.
- W3111291208 cites W2115709314 @default.
- W3111291208 cites W2122825543 @default.
- W3111291208 cites W2127890285 @default.
- W3111291208 cites W2128739912 @default.
- W3111291208 cites W2138019504 @default.
- W3111291208 cites W2258675270 @default.
- W3111291208 cites W2767128594 @default.
- W3111291208 cites W2771041964 @default.
- W3111291208 cites W2785884561 @default.
- W3111291208 cites W2801490189 @default.
- W3111291208 cites W2938774370 @default.
- W3111291208 cites W2943477262 @default.
- W3111291208 cites W2967782982 @default.
- W3111291208 doi "https://doi.org/10.1007/978-3-030-63061-4_24" @default.
- W3111291208 hasPublicationYear "2020" @default.
- W3111291208 type Work @default.
- W3111291208 sameAs 3111291208 @default.
- W3111291208 citedByCount "1" @default.
- W3111291208 countsByYear W31112912082023 @default.
- W3111291208 crossrefType "book-chapter" @default.
- W3111291208 hasAuthorship W3111291208A5023107971 @default.
- W3111291208 hasAuthorship W3111291208A5025206434 @default.
- W3111291208 hasAuthorship W3111291208A5025554206 @default.
- W3111291208 hasAuthorship W3111291208A5050926297 @default.
- W3111291208 hasAuthorship W3111291208A5056916227 @default.
- W3111291208 hasAuthorship W3111291208A5057794986 @default.
- W3111291208 hasAuthorship W3111291208A5064430711 @default.
- W3111291208 hasAuthorship W3111291208A5077039976 @default.
- W3111291208 hasAuthorship W3111291208A5080951501 @default.
- W3111291208 hasBestOaLocation W31112912082 @default.
- W3111291208 hasConcept C119857082 @default.
- W3111291208 hasConcept C121608353 @default.
- W3111291208 hasConcept C126322002 @default.
- W3111291208 hasConcept C136764020 @default.
- W3111291208 hasConcept C148483581 @default.
- W3111291208 hasConcept C153180895 @default.
- W3111291208 hasConcept C154945302 @default.
- W3111291208 hasConcept C2776135515 @default.
- W3111291208 hasConcept C2776608951 @default.
- W3111291208 hasConcept C2778559731 @default.
- W3111291208 hasConcept C37616216 @default.
- W3111291208 hasConcept C41008148 @default.
- W3111291208 hasConcept C530470458 @default.
- W3111291208 hasConcept C71924100 @default.
- W3111291208 hasConceptScore W3111291208C119857082 @default.
- W3111291208 hasConceptScore W3111291208C121608353 @default.
- W3111291208 hasConceptScore W3111291208C126322002 @default.
- W3111291208 hasConceptScore W3111291208C136764020 @default.
- W3111291208 hasConceptScore W3111291208C148483581 @default.
- W3111291208 hasConceptScore W3111291208C153180895 @default.
- W3111291208 hasConceptScore W3111291208C154945302 @default.
- W3111291208 hasConceptScore W3111291208C2776135515 @default.
- W3111291208 hasConceptScore W3111291208C2776608951 @default.
- W3111291208 hasConceptScore W3111291208C2778559731 @default.
- W3111291208 hasConceptScore W3111291208C37616216 @default.
- W3111291208 hasConceptScore W3111291208C41008148 @default.
- W3111291208 hasConceptScore W3111291208C530470458 @default.
- W3111291208 hasConceptScore W3111291208C71924100 @default.
- W3111291208 hasLocation W31112912081 @default.
- W3111291208 hasLocation W31112912082 @default.
- W3111291208 hasOpenAccess W3111291208 @default.
- W3111291208 hasPrimaryLocation W31112912081 @default.
- W3111291208 hasRelatedWork W2316780152 @default.
- W3111291208 hasRelatedWork W3087493185 @default.
- W3111291208 hasRelatedWork W3118634075 @default.
- W3111291208 hasRelatedWork W3129804828 @default.
- W3111291208 hasRelatedWork W3174196512 @default.
- W3111291208 hasRelatedWork W3200179079 @default.
- W3111291208 hasRelatedWork W4224059758 @default.
- W3111291208 hasRelatedWork W4285111941 @default.
- W3111291208 hasRelatedWork W4293525103 @default.
- W3111291208 hasRelatedWork W2345184372 @default.
- W3111291208 isParatext "false" @default.
- W3111291208 isRetracted "false" @default.
- W3111291208 magId "3111291208" @default.
- W3111291208 workType "book-chapter" @default.