Matches in SemOpenAlex for { <https://semopenalex.org/work/W4206339910> ?p ?o ?g. }
Showing items 1 to 89 of
89
with 100 items per page.
- W4206339910 abstract "Drawing support from an effective Medical Image Segmentation (MIS) is conducive to a substantial diagnostic basis for the physicians to identify the focus lesion in the patient body and give the subsequent clinical assessment of the patient status. Although various works have tried the challenging quantitative analysis problem, it is still difficult to conduct precise automatic segmentation, especially the soft tissue organs. In this decade, with the increased amount of available datasets, deep learning-based networks have achieved remarkable performance in image processing. Inspired by the state-of-the-art deep learning works, in this paper, we propose an end-to-end multi-layer network named RCGA-Net. It consists of an encoder-decoder backbone that integrates a coordinate attention mechanism based on space and channel and a global context extraction module to highlight more valuable information. To evaluate the performance of RCGA-Net, we apply it to different kinds of clinical and experimental MIS tasks to testify its generalization ability. Extensive experiments represent that our schema has taken the outperform or compatible results among the comparison methods group. Specifically, the numeric result of RCGA-Net on the pulmonary dataset has achieved a 99.12% optimum F1-score." @default.
- W4206339910 created "2022-01-25" @default.
- W4206339910 creator A5001619694 @default.
- W4206339910 creator A5003764862 @default.
- W4206339910 creator A5013493520 @default.
- W4206339910 creator A5034394457 @default.
- W4206339910 creator A5045463595 @default.
- W4206339910 creator A5059803686 @default.
- W4206339910 creator A5075559048 @default.
- W4206339910 date "2021-12-09" @default.
- W4206339910 modified "2023-09-29" @default.
- W4206339910 title "RCGA-Net: An Improved Multi-hybrid Attention Mechanism Network in Biomedical Image Segmentation" @default.
- W4206339910 cites W1903029394 @default.
- W4206339910 cites W2147484997 @default.
- W4206339910 cites W2412782625 @default.
- W4206339910 cites W2560260782 @default.
- W4206339910 cites W2576442780 @default.
- W4206339910 cites W2884436604 @default.
- W4206339910 cites W2908867979 @default.
- W4206339910 cites W2948580174 @default.
- W4206339910 cites W2962914239 @default.
- W4206339910 cites W2982220924 @default.
- W4206339910 cites W2987039128 @default.
- W4206339910 cites W2999580839 @default.
- W4206339910 cites W3004757123 @default.
- W4206339910 cites W3081752372 @default.
- W4206339910 cites W3103010481 @default.
- W4206339910 cites W3177052299 @default.
- W4206339910 cites W3187369430 @default.
- W4206339910 cites W3195525027 @default.
- W4206339910 cites W4206426944 @default.
- W4206339910 cites W4226351492 @default.
- W4206339910 doi "https://doi.org/10.1109/bibm52615.2021.9669413" @default.
- W4206339910 hasPublicationYear "2021" @default.
- W4206339910 type Work @default.
- W4206339910 citedByCount "2" @default.
- W4206339910 countsByYear W42063399102023 @default.
- W4206339910 crossrefType "proceedings-article" @default.
- W4206339910 hasAuthorship W4206339910A5001619694 @default.
- W4206339910 hasAuthorship W4206339910A5003764862 @default.
- W4206339910 hasAuthorship W4206339910A5013493520 @default.
- W4206339910 hasAuthorship W4206339910A5034394457 @default.
- W4206339910 hasAuthorship W4206339910A5045463595 @default.
- W4206339910 hasAuthorship W4206339910A5059803686 @default.
- W4206339910 hasAuthorship W4206339910A5075559048 @default.
- W4206339910 hasConcept C108583219 @default.
- W4206339910 hasConcept C111919701 @default.
- W4206339910 hasConcept C118505674 @default.
- W4206339910 hasConcept C119857082 @default.
- W4206339910 hasConcept C124504099 @default.
- W4206339910 hasConcept C151730666 @default.
- W4206339910 hasConcept C153180895 @default.
- W4206339910 hasConcept C154945302 @default.
- W4206339910 hasConcept C2779343474 @default.
- W4206339910 hasConcept C41008148 @default.
- W4206339910 hasConcept C52146309 @default.
- W4206339910 hasConcept C86803240 @default.
- W4206339910 hasConcept C89600930 @default.
- W4206339910 hasConceptScore W4206339910C108583219 @default.
- W4206339910 hasConceptScore W4206339910C111919701 @default.
- W4206339910 hasConceptScore W4206339910C118505674 @default.
- W4206339910 hasConceptScore W4206339910C119857082 @default.
- W4206339910 hasConceptScore W4206339910C124504099 @default.
- W4206339910 hasConceptScore W4206339910C151730666 @default.
- W4206339910 hasConceptScore W4206339910C153180895 @default.
- W4206339910 hasConceptScore W4206339910C154945302 @default.
- W4206339910 hasConceptScore W4206339910C2779343474 @default.
- W4206339910 hasConceptScore W4206339910C41008148 @default.
- W4206339910 hasConceptScore W4206339910C52146309 @default.
- W4206339910 hasConceptScore W4206339910C86803240 @default.
- W4206339910 hasConceptScore W4206339910C89600930 @default.
- W4206339910 hasFunder F4320321001 @default.
- W4206339910 hasFunder F4320321878 @default.
- W4206339910 hasLocation W42063399101 @default.
- W4206339910 hasOpenAccess W4206339910 @default.
- W4206339910 hasPrimaryLocation W42063399101 @default.
- W4206339910 hasRelatedWork W2790662084 @default.
- W4206339910 hasRelatedWork W3014300295 @default.
- W4206339910 hasRelatedWork W4223943233 @default.
- W4206339910 hasRelatedWork W4225161397 @default.
- W4206339910 hasRelatedWork W4285827401 @default.
- W4206339910 hasRelatedWork W4312200629 @default.
- W4206339910 hasRelatedWork W4360585206 @default.
- W4206339910 hasRelatedWork W4364306694 @default.
- W4206339910 hasRelatedWork W4380075502 @default.
- W4206339910 hasRelatedWork W4380086463 @default.
- W4206339910 isParatext "false" @default.
- W4206339910 isRetracted "false" @default.
- W4206339910 workType "article" @default.