Matches in SemOpenAlex for { <https://semopenalex.org/work/W4311463314> ?p ?o ?g. }
Showing items 1 to 68 of
68
with 100 items per page.
- W4311463314 abstract "Polyps segmentation is one of the key medical challenges in the gastrointestinal (GI) tract. Polyps segmentation provides the early-stage diagnosis of polyps which may lead to colon cancer in the GI tract. Deep learning models such as U-Net can segment polyps with good performance. But individual deep learning models may suffer from generalization problems. Deep ensemble learning combines the power of both deep and ensemble learning so that the final combined model has better generalization ability. In this paper, a bagging based U-Net architecture (BaggedUNet) is proposed to improve the polyps segmentation in GI-Tract. Our proposed BaggedUNet model trains several lighter U-Net architectures. Decisions from various models are then combined using majority voting. The proposed method is compared with recent deep learning architectures: U-Net and ResUNet++. The evaluation of models is performed using quantitative metrics including Dice coefficient and mean Intersection over Union (mIoU). The proposed BaggedUNet architecture was able to achieve 3 %-9 % improvement on different evaluation metrics on two publicly available datasets for polyps segmentation." @default.
- W4311463314 created "2022-12-26" @default.
- W4311463314 creator A5041637265 @default.
- W4311463314 creator A5042181468 @default.
- W4311463314 creator A5062239238 @default.
- W4311463314 date "2022-10-21" @default.
- W4311463314 modified "2023-09-24" @default.
- W4311463314 title "BaggedUNet: Deep Machine Vision approach for Polyps Segmentation in Gastrointestinal Tract" @default.
- W4311463314 cites W2008359794 @default.
- W4311463314 cites W2734349601 @default.
- W4311463314 cites W2740020919 @default.
- W4311463314 cites W2789758093 @default.
- W4311463314 cites W2796258071 @default.
- W4311463314 cites W2801197735 @default.
- W4311463314 cites W2902672843 @default.
- W4311463314 cites W2921231121 @default.
- W4311463314 cites W2965665272 @default.
- W4311463314 cites W2997286550 @default.
- W4311463314 cites W2998513439 @default.
- W4311463314 cites W2999580839 @default.
- W4311463314 cites W3017097154 @default.
- W4311463314 cites W3110536152 @default.
- W4311463314 cites W3186183909 @default.
- W4311463314 cites W4241005218 @default.
- W4311463314 doi "https://doi.org/10.1109/inmic56986.2022.9972945" @default.
- W4311463314 hasPublicationYear "2022" @default.
- W4311463314 type Work @default.
- W4311463314 citedByCount "0" @default.
- W4311463314 crossrefType "proceedings-article" @default.
- W4311463314 hasAuthorship W4311463314A5041637265 @default.
- W4311463314 hasAuthorship W4311463314A5042181468 @default.
- W4311463314 hasAuthorship W4311463314A5062239238 @default.
- W4311463314 hasConcept C108583219 @default.
- W4311463314 hasConcept C119857082 @default.
- W4311463314 hasConcept C124504099 @default.
- W4311463314 hasConcept C134306372 @default.
- W4311463314 hasConcept C153180895 @default.
- W4311463314 hasConcept C154945302 @default.
- W4311463314 hasConcept C177148314 @default.
- W4311463314 hasConcept C33923547 @default.
- W4311463314 hasConcept C41008148 @default.
- W4311463314 hasConcept C89600930 @default.
- W4311463314 hasConceptScore W4311463314C108583219 @default.
- W4311463314 hasConceptScore W4311463314C119857082 @default.
- W4311463314 hasConceptScore W4311463314C124504099 @default.
- W4311463314 hasConceptScore W4311463314C134306372 @default.
- W4311463314 hasConceptScore W4311463314C153180895 @default.
- W4311463314 hasConceptScore W4311463314C154945302 @default.
- W4311463314 hasConceptScore W4311463314C177148314 @default.
- W4311463314 hasConceptScore W4311463314C33923547 @default.
- W4311463314 hasConceptScore W4311463314C41008148 @default.
- W4311463314 hasConceptScore W4311463314C89600930 @default.
- W4311463314 hasLocation W43114633141 @default.
- W4311463314 hasOpenAccess W4311463314 @default.
- W4311463314 hasPrimaryLocation W43114633141 @default.
- W4311463314 hasRelatedWork W2897195263 @default.
- W4311463314 hasRelatedWork W2948658236 @default.
- W4311463314 hasRelatedWork W2960184797 @default.
- W4311463314 hasRelatedWork W2972093541 @default.
- W4311463314 hasRelatedWork W3115553566 @default.
- W4311463314 hasRelatedWork W3135174555 @default.
- W4311463314 hasRelatedWork W3209779739 @default.
- W4311463314 hasRelatedWork W4285827401 @default.
- W4311463314 hasRelatedWork W4293211451 @default.
- W4311463314 hasRelatedWork W4319300655 @default.
- W4311463314 isParatext "false" @default.
- W4311463314 isRetracted "false" @default.
- W4311463314 workType "article" @default.