FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3048690458> ?p ?o ?g. }

• W3048690458 endingPage "235045" @default.
• W3048690458 startingPage "235045" @default.
• W3048690458 abstract "To develop and evaluate a multi-path synergic fusion (MSF) deep neural network model for breast mass classification using digital breast tomosynthesis (DBT).We retrospectively collected 441 patients who had undergone DBT in which the regions of interest (ROIs) covering the malignant/benign breast mass were extracted for model training and validation. In the proposed MSF framework, three multifaceted representations of the breast mass (gross mass, overview, and mass background) are extracted from the ROIs and independently processed by a multi-scale multi-level features enforced DenseNet (MMFED). The three MMFED sub-models are finally fused at the decision level to generate the final prediction. The advantages of the MMFED over the original DenseNet, as well as different fusion strategies embedded in MSF, were comprehensively compared.The MMFED was observed to be superior to the original DenseNet, and multiple channel fusions in the MSF outperformed the single-channel MMFED and double-channel fusion with the best classification scores of area under the receiver operating characteristic (ROC) curve (87.03%), Accuracy (81.29%), Sensitivity (74.57%), and Specificity (84.53%) via the weighted fusion method embedded in MSF. The decision level fusion-based MSF was significantly better (in terms of the ROC curve) than the feature concatenation-based fusion (p< 0.05), the single MMFED using a fused three-channel image (p< 0.04), and the multiple MMFED end-to-end training (p< 0.004).Integrating multifaceted representations of the breast mass tends to increase benign/malignant mass classification performance and the proposed methodology was verified to be a promising tool to assist in clinical breast cancer screening." @default.
• W3048690458 created "2020-08-18" @default.
• W3048690458 creator A5004951824 @default.
• W3048690458 creator A5010951311 @default.
• W3048690458 creator A5013607405 @default.
• W3048690458 creator A5021121972 @default.
• W3048690458 creator A5022526821 @default.
• W3048690458 creator A5022599233 @default.
• W3048690458 creator A5025079276 @default.
• W3048690458 creator A5037779332 @default.
• W3048690458 creator A5042302046 @default.
• W3048690458 creator A5065480548 @default.
• W3048690458 creator A5068304714 @default.
• W3048690458 creator A5069975129 @default.
• W3048690458 creator A5074518598 @default.
• W3048690458 date "2020-12-01" @default.
• W3048690458 modified "2023-09-23" @default.
• W3048690458 title "Multi-path synergic fusion deep neural network framework for breast mass classification using digital breast tomosynthesis" @default.
• W3048690458 cites W1972626213 @default.
• W3048690458 cites W1978792808 @default.
• W3048690458 cites W2039434526 @default.
• W3048690458 cites W2050883594 @default.
• W3048690458 cites W2065133042 @default.
• W3048690458 cites W2070644180 @default.
• W3048690458 cites W2079019493 @default.
• W3048690458 cites W2111470548 @default.
• W3048690458 cites W2116825371 @default.
• W3048690458 cites W2123318149 @default.
• W3048690458 cites W2129044112 @default.
• W3048690458 cites W2133195316 @default.
• W3048690458 cites W2144827794 @default.
• W3048690458 cites W2148143831 @default.
• W3048690458 cites W2149291062 @default.
• W3048690458 cites W2166609686 @default.
• W3048690458 cites W2253429366 @default.
• W3048690458 cites W2338271170 @default.
• W3048690458 cites W2471976375 @default.
• W3048690458 cites W2520642424 @default.
• W3048690458 cites W2521782317 @default.
• W3048690458 cites W2607388523 @default.
• W3048690458 cites W2753801833 @default.
• W3048690458 cites W2767630494 @default.
• W3048690458 cites W2768529727 @default.
• W3048690458 cites W2775577467 @default.
• W3048690458 cites W2792982570 @default.
• W3048690458 cites W2793200524 @default.
• W3048690458 cites W2796345789 @default.
• W3048690458 cites W2800539391 @default.
• W3048690458 cites W2803731371 @default.
• W3048690458 cites W2806707478 @default.
• W3048690458 cites W2807574682 @default.
• W3048690458 cites W2885841583 @default.
• W3048690458 cites W2889135240 @default.
• W3048690458 cites W2889646458 @default.
• W3048690458 cites W2897755679 @default.
• W3048690458 cites W2904721614 @default.
• W3048690458 cites W2907224328 @default.
• W3048690458 cites W2909843210 @default.
• W3048690458 cites W2923581338 @default.
• W3048690458 cites W2933084664 @default.
• W3048690458 cites W2965342810 @default.
• W3048690458 cites W2965845428 @default.
• W3048690458 cites W2988125122 @default.
• W3048690458 cites W3003475598 @default.
• W3048690458 cites W3004982181 @default.
• W3048690458 doi "https://doi.org/10.1088/1361-6560/abaeb7" @default.
• W3048690458 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33274728" @default.
• W3048690458 hasPublicationYear "2020" @default.
• W3048690458 type Work @default.
• W3048690458 sameAs 3048690458 @default.
• W3048690458 citedByCount "6" @default.
• W3048690458 countsByYear W30486904582021 @default.
• W3048690458 countsByYear W30486904582022 @default.
• W3048690458 countsByYear W30486904582023 @default.
• W3048690458 crossrefType "journal-article" @default.
• W3048690458 hasAuthorship W3048690458A5004951824 @default.
• W3048690458 hasAuthorship W3048690458A5010951311 @default.
• W3048690458 hasAuthorship W3048690458A5013607405 @default.
• W3048690458 hasAuthorship W3048690458A5021121972 @default.
• W3048690458 hasAuthorship W3048690458A5022526821 @default.
• W3048690458 hasAuthorship W3048690458A5022599233 @default.
• W3048690458 hasAuthorship W3048690458A5025079276 @default.
• W3048690458 hasAuthorship W3048690458A5037779332 @default.
• W3048690458 hasAuthorship W3048690458A5042302046 @default.
• W3048690458 hasAuthorship W3048690458A5065480548 @default.
• W3048690458 hasAuthorship W3048690458A5068304714 @default.
• W3048690458 hasAuthorship W3048690458A5069975129 @default.
• W3048690458 hasAuthorship W3048690458A5074518598 @default.
• W3048690458 hasConcept C114614502 @default.
• W3048690458 hasConcept C119857082 @default.
• W3048690458 hasConcept C127162648 @default.
• W3048690458 hasConcept C138885662 @default.
• W3048690458 hasConcept C153180895 @default.
• W3048690458 hasConcept C154945302 @default.
• W3048690458 hasConcept C158525013 @default.
• W3048690458 hasConcept C2776401178 @default.
• W3048690458 hasConcept C33923547 @default.
• W3048690458 hasConcept C41008148 @default.

• next