Matches in SemOpenAlex for { <https://semopenalex.org/work/W3130482904> ?p ?o ?g. }
- W3130482904 endingPage "33768" @default.
- W3130482904 startingPage "33756" @default.
- W3130482904 abstract "Medical image fusion technology has been widely used in clinical practice by doctors to better understand lesion regions through the fusion of multiparametric medical images. This paper proposes an automated fusion method based on a U-Net. Through neural network learning, a weight distribution is generated based on the relationship between the image feature information and the multifocus training target. The MRI image pair of prostate cancer (axial T2-weighted and ADC map) is fused using a strategy based on local similarity and Gaussian pyramid transformation. Experimental results show that the fusion method can enhance the appearance of prostate cancer in terms of both visual quality and objective evaluation." @default.
- W3130482904 created "2021-03-01" @default.
- W3130482904 creator A5007910796 @default.
- W3130482904 creator A5011877804 @default.
- W3130482904 creator A5017389204 @default.
- W3130482904 creator A5026269938 @default.
- W3130482904 creator A5042956973 @default.
- W3130482904 creator A5050143212 @default.
- W3130482904 creator A5080566166 @default.
- W3130482904 date "2021-01-01" @default.
- W3130482904 modified "2023-09-23" @default.
- W3130482904 title "Application of U-Net Based Multiparameter Magnetic Resonance Image Fusion in the Diagnosis of Prostate Cancer" @default.
- W3130482904 cites W1964641132 @default.
- W3130482904 cites W1971760642 @default.
- W3130482904 cites W1976428949 @default.
- W3130482904 cites W2041562209 @default.
- W3130482904 cites W2051306500 @default.
- W3130482904 cites W2066667210 @default.
- W3130482904 cites W2081195086 @default.
- W3130482904 cites W2090507060 @default.
- W3130482904 cites W2102283130 @default.
- W3130482904 cites W2112224175 @default.
- W3130482904 cites W2116456749 @default.
- W3130482904 cites W2133665775 @default.
- W3130482904 cites W2148673597 @default.
- W3130482904 cites W2164159666 @default.
- W3130482904 cites W2258043314 @default.
- W3130482904 cites W2289413700 @default.
- W3130482904 cites W2464701508 @default.
- W3130482904 cites W2489975106 @default.
- W3130482904 cites W2546768712 @default.
- W3130482904 cites W2582220857 @default.
- W3130482904 cites W2584483805 @default.
- W3130482904 cites W2589644515 @default.
- W3130482904 cites W2592312604 @default.
- W3130482904 cites W2744198280 @default.
- W3130482904 cites W2751909359 @default.
- W3130482904 cites W2763013859 @default.
- W3130482904 cites W2767969013 @default.
- W3130482904 cites W2790048445 @default.
- W3130482904 cites W2792029318 @default.
- W3130482904 cites W2798401174 @default.
- W3130482904 cites W2805412928 @default.
- W3130482904 cites W2891815950 @default.
- W3130482904 cites W2900936384 @default.
- W3130482904 cites W2901776819 @default.
- W3130482904 cites W2903896812 @default.
- W3130482904 cites W2912551047 @default.
- W3130482904 cites W2913182391 @default.
- W3130482904 cites W2922129118 @default.
- W3130482904 cites W2922985417 @default.
- W3130482904 cites W2940940727 @default.
- W3130482904 cites W2944757081 @default.
- W3130482904 cites W2944891925 @default.
- W3130482904 cites W2951901661 @default.
- W3130482904 cites W2962914239 @default.
- W3130482904 cites W2967694147 @default.
- W3130482904 cites W2983888060 @default.
- W3130482904 cites W2995275609 @default.
- W3130482904 cites W2997144603 @default.
- W3130482904 cites W3015396480 @default.
- W3130482904 cites W3021363125 @default.
- W3130482904 doi "https://doi.org/10.1109/access.2021.3061078" @default.
- W3130482904 hasPublicationYear "2021" @default.
- W3130482904 type Work @default.
- W3130482904 sameAs 3130482904 @default.
- W3130482904 citedByCount "5" @default.
- W3130482904 countsByYear W31304829042021 @default.
- W3130482904 countsByYear W31304829042022 @default.
- W3130482904 countsByYear W31304829042023 @default.
- W3130482904 crossrefType "journal-article" @default.
- W3130482904 hasAuthorship W3130482904A5007910796 @default.
- W3130482904 hasAuthorship W3130482904A5011877804 @default.
- W3130482904 hasAuthorship W3130482904A5017389204 @default.
- W3130482904 hasAuthorship W3130482904A5026269938 @default.
- W3130482904 hasAuthorship W3130482904A5042956973 @default.
- W3130482904 hasAuthorship W3130482904A5050143212 @default.
- W3130482904 hasAuthorship W3130482904A5080566166 @default.
- W3130482904 hasBestOaLocation W31304829041 @default.
- W3130482904 hasConcept C103278499 @default.
- W3130482904 hasConcept C104317684 @default.
- W3130482904 hasConcept C115961682 @default.
- W3130482904 hasConcept C121608353 @default.
- W3130482904 hasConcept C126322002 @default.
- W3130482904 hasConcept C126838900 @default.
- W3130482904 hasConcept C138885662 @default.
- W3130482904 hasConcept C142575187 @default.
- W3130482904 hasConcept C143409427 @default.
- W3130482904 hasConcept C153180895 @default.
- W3130482904 hasConcept C154945302 @default.
- W3130482904 hasConcept C158525013 @default.
- W3130482904 hasConcept C185592680 @default.
- W3130482904 hasConcept C204241405 @default.
- W3130482904 hasConcept C2524010 @default.
- W3130482904 hasConcept C2776401178 @default.
- W3130482904 hasConcept C2780192828 @default.
- W3130482904 hasConcept C31972630 @default.
- W3130482904 hasConcept C33923547 @default.