FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4321372012> ?p ?o ?g. }

• W4321372012 endingPage "2346" @default.
• W4321372012 startingPage "2346" @default.
• W4321372012 abstract "Brain tumors are among the deadliest forms of cancer, characterized by abnormal proliferation of brain cells. While early identification of brain tumors can greatly aid in their therapy, the process of manual segmentation performed by expert doctors, which is often time-consuming, tedious, and prone to human error, can act as a bottleneck in the diagnostic process. This motivates the development of automated algorithms for brain tumor segmentation. However, accurately segmenting the enhanced and core tumor regions is complicated due to high levels of inter- and intra-tumor heterogeneity in terms of texture, morphology, and shape. This study proposes a fully automatic method called the selective deeply supervised multi-scale attention network (SDS-MSA-Net) for segmenting brain tumor regions using a multi-scale attention network with novel selective deep supervision (SDS) mechanisms for training. The method utilizes a 3D input composed of five consecutive slices, in addition to a 2D slice, to maintain sequential information. The proposed multi-scale architecture includes two encoding units to extract meaningful global and local features from the 3D and 2D inputs, respectively. These coarse features are then passed through attention units to filter out redundant information by assigning lower weights. The refined features are fed into a decoder block, which upscales the features at various levels while learning patterns relevant to all tumor regions. The SDS block is introduced to immediately upscale features from intermediate layers of the decoder, with the aim of producing segmentations of the whole, enhanced, and core tumor regions. The proposed framework was evaluated on the BraTS2020 dataset and showed improved performance in brain tumor region segmentation, particularly in the segmentation of the core and enhancing tumor regions, demonstrating the effectiveness of the proposed approach. Our code is publicly available." @default.
• W4321372012 created "2023-02-21" @default.
• W4321372012 creator A5016767276 @default.
• W4321372012 creator A5024880137 @default.
• W4321372012 creator A5037574053 @default.
• W4321372012 creator A5047197696 @default.
• W4321372012 creator A5065073249 @default.
• W4321372012 date "2023-02-20" @default.
• W4321372012 modified "2023-09-30" @default.
• W4321372012 title "Selective Deeply Supervised Multi-Scale Attention Network for Brain Tumor Segmentation" @default.
• W4321372012 cites W1884191083 @default.
• W4321372012 cites W2310992461 @default.
• W4321372012 cites W2587828787 @default.
• W4321372012 cites W2613041730 @default.
• W4321372012 cites W2613456556 @default.
• W4321372012 cites W2732063980 @default.
• W4321372012 cites W2751909359 @default.
• W4321372012 cites W2891118631 @default.
• W4321372012 cites W2938558375 @default.
• W4321372012 cites W2963046541 @default.
• W4321372012 cites W2963495494 @default.
• W4321372012 cites W2963717741 @default.
• W4321372012 cites W3021137017 @default.
• W4321372012 cites W3028279406 @default.
• W4321372012 cites W3045605181 @default.
• W4321372012 cites W3088910991 @default.
• W4321372012 cites W3142717613 @default.
• W4321372012 cites W3144727859 @default.
• W4321372012 cites W3145132551 @default.
• W4321372012 cites W3149990595 @default.
• W4321372012 cites W3150452586 @default.
• W4321372012 cites W3164866755 @default.
• W4321372012 cites W3174184163 @default.
• W4321372012 cites W4210347885 @default.
• W4321372012 cites W4226488683 @default.
• W4321372012 cites W4284973149 @default.
• W4321372012 cites W4313531537 @default.
• W4321372012 doi "https://doi.org/10.3390/s23042346" @default.
• W4321372012 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36850942" @default.
• W4321372012 hasPublicationYear "2023" @default.
• W4321372012 type Work @default.
• W4321372012 citedByCount "1" @default.
• W4321372012 countsByYear W43213720122023 @default.
• W4321372012 crossrefType "journal-article" @default.
• W4321372012 hasAuthorship W4321372012A5016767276 @default.
• W4321372012 hasAuthorship W4321372012A5024880137 @default.
• W4321372012 hasAuthorship W4321372012A5037574053 @default.
• W4321372012 hasAuthorship W4321372012A5047197696 @default.
• W4321372012 hasAuthorship W4321372012A5065073249 @default.
• W4321372012 hasBestOaLocation W43213720121 @default.
• W4321372012 hasConcept C106131492 @default.
• W4321372012 hasConcept C111919701 @default.
• W4321372012 hasConcept C116834253 @default.
• W4321372012 hasConcept C121332964 @default.
• W4321372012 hasConcept C125308379 @default.
• W4321372012 hasConcept C125411270 @default.
• W4321372012 hasConcept C144133560 @default.
• W4321372012 hasConcept C149635348 @default.
• W4321372012 hasConcept C153180895 @default.
• W4321372012 hasConcept C154945302 @default.
• W4321372012 hasConcept C162853370 @default.
• W4321372012 hasConcept C2524010 @default.
• W4321372012 hasConcept C2777210771 @default.
• W4321372012 hasConcept C2778755073 @default.
• W4321372012 hasConcept C2780513914 @default.
• W4321372012 hasConcept C31972630 @default.
• W4321372012 hasConcept C33923547 @default.
• W4321372012 hasConcept C41008148 @default.
• W4321372012 hasConcept C59822182 @default.
• W4321372012 hasConcept C62520636 @default.
• W4321372012 hasConcept C86803240 @default.
• W4321372012 hasConcept C89600930 @default.
• W4321372012 hasConcept C98045186 @default.
• W4321372012 hasConceptScore W4321372012C106131492 @default.
• W4321372012 hasConceptScore W4321372012C111919701 @default.
• W4321372012 hasConceptScore W4321372012C116834253 @default.
• W4321372012 hasConceptScore W4321372012C121332964 @default.
• W4321372012 hasConceptScore W4321372012C125308379 @default.
• W4321372012 hasConceptScore W4321372012C125411270 @default.
• W4321372012 hasConceptScore W4321372012C144133560 @default.
• W4321372012 hasConceptScore W4321372012C149635348 @default.
• W4321372012 hasConceptScore W4321372012C153180895 @default.
• W4321372012 hasConceptScore W4321372012C154945302 @default.
• W4321372012 hasConceptScore W4321372012C162853370 @default.
• W4321372012 hasConceptScore W4321372012C2524010 @default.
• W4321372012 hasConceptScore W4321372012C2777210771 @default.
• W4321372012 hasConceptScore W4321372012C2778755073 @default.
• W4321372012 hasConceptScore W4321372012C2780513914 @default.
• W4321372012 hasConceptScore W4321372012C31972630 @default.
• W4321372012 hasConceptScore W4321372012C33923547 @default.
• W4321372012 hasConceptScore W4321372012C41008148 @default.
• W4321372012 hasConceptScore W4321372012C59822182 @default.
• W4321372012 hasConceptScore W4321372012C62520636 @default.
• W4321372012 hasConceptScore W4321372012C86803240 @default.
• W4321372012 hasConceptScore W4321372012C89600930 @default.
• W4321372012 hasConceptScore W4321372012C98045186 @default.
• W4321372012 hasIssue "4" @default.
• W4321372012 hasLocation W43213720121 @default.

• next