FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2034838676> ?p ?o ?g. }

• W2034838676 endingPage "351" @default.
• W2034838676 startingPage "339" @default.
• W2034838676 abstract "In this preliminary study, recently developed unsupervised nonlinear dimension reduction (DR) and data representation techniques were applied to computer-extracted breast lesion feature spaces across three separate imaging modalities: Ultrasound (U.S.) with 1126 cases, dynamic contrast enhanced magnetic resonance imaging with 356 cases, and full-field digital mammography with 245 cases. Two methods for nonlinear DR were explored: Laplacian eigenmaps [M. Belkin and P. Niyogi, Laplacian eigenmaps for dimensionality reduction and data representation, Neural Comput. 15, 1373-1396 (2003)] and t-distributed stochastic neighbor embedding (t-SNE) [L. van der Maaten and G. Hinton, Visualizing data using t-SNE, J. Mach. Learn. Res. 9, 2579-2605 (2008)].These methods attempt to map originally high dimensional feature spaces to more human interpretable lower dimensional spaces while preserving both local and global information. The properties of these methods as applied to breast computer-aided diagnosis (CADx) were evaluated in the context of malignancy classification performance as well as in the visual inspection of the sparseness within the two-dimensional and three-dimensional mappings. Classification performance was estimated by using the reduced dimension mapped feature output as input into both linear and nonlinear classifiers: Markov chain Monte Carlo based Bayesian artificial neural network (MCMC-BANN) and linear discriminant analysis. The new techniques were compared to previously developed breast CADx methodologies, including automatic relevance determination and linear stepwise (LSW) feature selection, as well as a linear DR method based on principal component analysis. Using ROC analysis and 0.632+bootstrap validation, 95% empirical confidence intervals were computed for the each classifier's AUC performance.In the large U.S. data set, sample high performance results include, AUC0.632+ = 0.88 with 95% empirical bootstrap interval [0.787;0.895] for 13 ARD selected features and AUC0.632+ = 0.87 with interval [0.817;0.906] for four LSW selected features compared to 4D t-SNE mapping (from the original 81D feature space) giving AUC0.632+ = 0.90 with interval [0.847;0.919], all using the MCMC-BANN.Preliminary results appear to indicate capability for the new methods to match or exceed classification performance of current advanced breast lesion CADx algorithms. While not appropriate as a complete replacement of feature selection in CADx problems, DR techniques offer a complementary approach, which can aid elucidation of additional properties associated with the data. Specifically, the new techniques were shown to possess the added benefit of delivering sparse lower dimensional representations for visual interpretation, revealing intricate data structure of the feature space." @default.
• W2034838676 created "2016-06-24" @default.
• W2034838676 creator A5024321936 @default.
• W2034838676 creator A5029283101 @default.
• W2034838676 creator A5049042648 @default.
• W2034838676 creator A5063878800 @default.
• W2034838676 creator A5069788317 @default.
• W2034838676 creator A5087485509 @default.
• W2034838676 date "2009-12-22" @default.
• W2034838676 modified "2023-10-14" @default.
• W2034838676 title "Exploring nonlinear feature space dimension reduction and data representation in breast CADx with Laplacian eigenmaps and ‐SNE" @default.
• W2034838676 cites W1485550703 @default.
• W2034838676 cites W1567512734 @default.
• W2034838676 cites W1968114652 @default.
• W2034838676 cites W1974536055 @default.
• W2034838676 cites W1985804137 @default.
• W2034838676 cites W1989033067 @default.
• W2034838676 cites W1989368986 @default.
• W2034838676 cites W1992489994 @default.
• W2034838676 cites W2001180220 @default.
• W2034838676 cites W2002844845 @default.
• W2034838676 cites W2004980192 @default.
• W2034838676 cites W2017352979 @default.
• W2034838676 cites W2018490275 @default.
• W2034838676 cites W2022165721 @default.
• W2034838676 cites W2024783313 @default.
• W2034838676 cites W2038037430 @default.
• W2034838676 cites W2059165786 @default.
• W2034838676 cites W2059192589 @default.
• W2034838676 cites W2061568180 @default.
• W2034838676 cites W2076118331 @default.
• W2034838676 cites W2086268663 @default.
• W2034838676 cites W2089918690 @default.
• W2034838676 cites W2092068359 @default.
• W2034838676 cites W2097308346 @default.
• W2034838676 cites W2118291747 @default.
• W2034838676 cites W2124965119 @default.
• W2034838676 cites W2152661346 @default.
• W2034838676 cites W2157117929 @default.
• W2034838676 cites W2157825442 @default.
• W2034838676 cites W2167866422 @default.
• W2034838676 cites W2169388041 @default.
• W2034838676 cites W4212863985 @default.
• W2034838676 cites W4238805501 @default.
• W2034838676 cites W4250236131 @default.
• W2034838676 cites W98828706 @default.
• W2034838676 doi "https://doi.org/10.1118/1.3267037" @default.
• W2034838676 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/2807447" @default.
• W2034838676 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/20175497" @default.
• W2034838676 hasPublicationYear "2009" @default.
• W2034838676 type Work @default.
• W2034838676 sameAs 2034838676 @default.
• W2034838676 citedByCount "115" @default.
• W2034838676 countsByYear W20348386762012 @default.
• W2034838676 countsByYear W20348386762013 @default.
• W2034838676 countsByYear W20348386762014 @default.
• W2034838676 countsByYear W20348386762015 @default.
• W2034838676 countsByYear W20348386762016 @default.
• W2034838676 countsByYear W20348386762017 @default.
• W2034838676 countsByYear W20348386762018 @default.
• W2034838676 countsByYear W20348386762019 @default.
• W2034838676 countsByYear W20348386762020 @default.
• W2034838676 countsByYear W20348386762021 @default.
• W2034838676 countsByYear W20348386762022 @default.
• W2034838676 countsByYear W20348386762023 @default.
• W2034838676 crossrefType "journal-article" @default.
• W2034838676 hasAuthorship W2034838676A5024321936 @default.
• W2034838676 hasAuthorship W2034838676A5029283101 @default.
• W2034838676 hasAuthorship W2034838676A5049042648 @default.
• W2034838676 hasAuthorship W2034838676A5063878800 @default.
• W2034838676 hasAuthorship W2034838676A5069788317 @default.
• W2034838676 hasAuthorship W2034838676A5087485509 @default.
• W2034838676 hasBestOaLocation W20348386762 @default.
• W2034838676 hasConcept C138885662 @default.
• W2034838676 hasConcept C151730666 @default.
• W2034838676 hasConcept C151876577 @default.
• W2034838676 hasConcept C153180895 @default.
• W2034838676 hasConcept C154945302 @default.
• W2034838676 hasConcept C27438332 @default.
• W2034838676 hasConcept C2776401178 @default.
• W2034838676 hasConcept C2779343474 @default.
• W2034838676 hasConcept C33923547 @default.
• W2034838676 hasConcept C41008148 @default.
• W2034838676 hasConcept C41895202 @default.
• W2034838676 hasConcept C69738355 @default.
• W2034838676 hasConcept C70518039 @default.
• W2034838676 hasConcept C83665646 @default.
• W2034838676 hasConcept C86803240 @default.
• W2034838676 hasConceptScore W2034838676C138885662 @default.
• W2034838676 hasConceptScore W2034838676C151730666 @default.
• W2034838676 hasConceptScore W2034838676C151876577 @default.
• W2034838676 hasConceptScore W2034838676C153180895 @default.
• W2034838676 hasConceptScore W2034838676C154945302 @default.
• W2034838676 hasConceptScore W2034838676C27438332 @default.
• W2034838676 hasConceptScore W2034838676C2776401178 @default.
• W2034838676 hasConceptScore W2034838676C2779343474 @default.
• W2034838676 hasConceptScore W2034838676C33923547 @default.
• W2034838676 hasConceptScore W2034838676C41008148 @default.

• next