FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386630433> ?p ?o ?g. }

• W4386630433 endingPage "195019" @default.
• W4386630433 startingPage "195019" @default.
• W4386630433 abstract "Abstract Objective . In brain tumor segmentation tasks, the convolutional neural network (CNN) or transformer is usually acted as the encoder since the encoder is necessary to be used. On one hand, the convolution operation of CNN has advantages of extracting local information although its performance of obtaining global expressions is bad. On the other hand, the attention mechanism of the transformer is good at establishing remote dependencies while it is lacking in the ability to extract high-precision local information. Either high precision local information or global contextual information is crucial in brain tumor segmentation tasks. The aim of this paper is to propose a brain tumor segmentation model that can simultaneously extract and fuse high-precision local and global contextual information. Approach . We propose a network model DE-Uformer with dual encoders to obtain local features and global representations using both CNN encoder and Transformer encoder. On the basis of this, we further propose the nested encoder-aware feature fusion (NEaFF) module for effective deep fusion of the information under each dimension. It may establishe remote dependencies of features under a single encoder via the spatial attention Transformer. Meanwhile ,it also investigates how features extracted from two encoders are related with the cross-encoder attention transformer. Main results . The proposed algorithm segmentation have been performed on BraTS2020 dataset and private meningioma dataset. Results show that it is significantly better than current state-of-the-art brain tumor segmentation methods. Significance . The method proposed in this paper greatly improves the accuracy of brain tumor segmentation. This advancement helps healthcare professionals perform a more comprehensive analysis and assessment of brain tumors, thereby improving diagnostic accuracy and reliability. This fully automated brain model segmentation model with high accuracy is of great significance for critical decisions made by physicians in selecting treatment strategies and preoperative planning." @default.
• W4386630433 created "2023-09-13" @default.
• W4386630433 creator A5012302920 @default.
• W4386630433 creator A5041010870 @default.
• W4386630433 creator A5041270266 @default.
• W4386630433 creator A5042317614 @default.
• W4386630433 creator A5090283821 @default.
• W4386630433 date "2023-09-26" @default.
• W4386630433 modified "2023-09-28" @default.
• W4386630433 title "DE-UFormer: U-shaped dual encoder architectures for brain tumor segmentation" @default.
• W4386630433 cites W1641498739 @default.
• W4386630433 cites W1831416753 @default.
• W4386630433 cites W2043007123 @default.
• W4386630433 cites W2118386984 @default.
• W4386630433 cites W2123498585 @default.
• W4386630433 cites W2135465849 @default.
• W4386630433 cites W2136573752 @default.
• W4386630433 cites W2147484997 @default.
• W4386630433 cites W2154981222 @default.
• W4386630433 cites W2464708700 @default.
• W4386630433 cites W2581082771 @default.
• W4386630433 cites W2769848455 @default.
• W4386630433 cites W2810024032 @default.
• W4386630433 cites W2952378269 @default.
• W4386630433 cites W2953129827 @default.
• W4386630433 cites W2963881378 @default.
• W4386630433 cites W2964227007 @default.
• W4386630433 cites W2993853826 @default.
• W4386630433 cites W3103010481 @default.
• W4386630433 cites W3112701542 @default.
• W4386630433 cites W3118177885 @default.
• W4386630433 cites W3187574759 @default.
• W4386630433 cites W4211132876 @default.
• W4386630433 cites W4221163766 @default.
• W4386630433 cites W4283080861 @default.
• W4386630433 cites W4285600307 @default.
• W4386630433 cites W4295940432 @default.
• W4386630433 cites W4321232185 @default.
• W4386630433 doi "https://doi.org/10.1088/1361-6560/acf911" @default.
• W4386630433 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37699403" @default.
• W4386630433 hasPublicationYear "2023" @default.
• W4386630433 type Work @default.
• W4386630433 citedByCount "0" @default.
• W4386630433 crossrefType "journal-article" @default.
• W4386630433 hasAuthorship W4386630433A5012302920 @default.
• W4386630433 hasAuthorship W4386630433A5041010870 @default.
• W4386630433 hasAuthorship W4386630433A5041270266 @default.
• W4386630433 hasAuthorship W4386630433A5042317614 @default.
• W4386630433 hasAuthorship W4386630433A5090283821 @default.
• W4386630433 hasConcept C101738243 @default.
• W4386630433 hasConcept C108583219 @default.
• W4386630433 hasConcept C111919701 @default.
• W4386630433 hasConcept C118505674 @default.
• W4386630433 hasConcept C121332964 @default.
• W4386630433 hasConcept C153180895 @default.
• W4386630433 hasConcept C154945302 @default.
• W4386630433 hasConcept C165801399 @default.
• W4386630433 hasConcept C31972630 @default.
• W4386630433 hasConcept C41008148 @default.
• W4386630433 hasConcept C62520636 @default.
• W4386630433 hasConcept C66322947 @default.
• W4386630433 hasConcept C81363708 @default.
• W4386630433 hasConcept C89600930 @default.
• W4386630433 hasConceptScore W4386630433C101738243 @default.
• W4386630433 hasConceptScore W4386630433C108583219 @default.
• W4386630433 hasConceptScore W4386630433C111919701 @default.
• W4386630433 hasConceptScore W4386630433C118505674 @default.
• W4386630433 hasConceptScore W4386630433C121332964 @default.
• W4386630433 hasConceptScore W4386630433C153180895 @default.
• W4386630433 hasConceptScore W4386630433C154945302 @default.
• W4386630433 hasConceptScore W4386630433C165801399 @default.
• W4386630433 hasConceptScore W4386630433C31972630 @default.
• W4386630433 hasConceptScore W4386630433C41008148 @default.
• W4386630433 hasConceptScore W4386630433C62520636 @default.
• W4386630433 hasConceptScore W4386630433C66322947 @default.
• W4386630433 hasConceptScore W4386630433C81363708 @default.
• W4386630433 hasConceptScore W4386630433C89600930 @default.
• W4386630433 hasIssue "19" @default.
• W4386630433 hasLocation W43866304331 @default.
• W4386630433 hasLocation W43866304332 @default.
• W4386630433 hasOpenAccess W4386630433 @default.
• W4386630433 hasPrimaryLocation W43866304331 @default.
• W4386630433 hasRelatedWork W2669956259 @default.
• W4386630433 hasRelatedWork W2731899572 @default.
• W4386630433 hasRelatedWork W2939353110 @default.
• W4386630433 hasRelatedWork W2998168123 @default.
• W4386630433 hasRelatedWork W3116150086 @default.
• W4386630433 hasRelatedWork W3133861977 @default.
• W4386630433 hasRelatedWork W4200173597 @default.
• W4386630433 hasRelatedWork W4287995534 @default.
• W4386630433 hasRelatedWork W4312417841 @default.
• W4386630433 hasRelatedWork W4321369474 @default.
• W4386630433 hasVolume "68" @default.
• W4386630433 isParatext "false" @default.
• W4386630433 isRetracted "false" @default.
• W4386630433 workType "article" @default.