FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4294775257> ?p ?o ?g. }

• W4294775257 endingPage "105985" @default.
• W4294775257 startingPage "105985" @default.
• W4294775257 abstract "In recent years, deep learning (DL) has been recognized very useful in the semantic segmentation of biomedical images. Such an application, however, is significantly hindered by the lack of pixel-wise annotations. In this work, we propose a data pair generative adversarial network (DPGAN) for the purpose of synthesizing concurrently the diverse biomedical images and the segmentation labels from random latent vectors. First, a hierarchical structure is constructed consisting of three variational auto-encoder generative adversarial networks (VAEGANs) with an extra discriminator. Subsequently, to alleviate the influence from the imbalance between lesions and non-lesions areas in biomedical segmentation data sets, we divide the DPGAN into three stages, namely, background stage, mask stage and advanced stage, with each stage deploying a VAEGAN. In such a way, a large number of new segmentation data pairs are generated from random latent vectors and then used to augment the original data sets. Finally, to validate the effectiveness of the proposed DPGAN, experiments are carried out on a vestibular schwannoma data set, a kidney tumor data set and a skin cancer data set. The results indicate that, in comparison to other state-of-the-art GAN-based methods, the proposed DPGAN shows better performance in the generative quality, and meanwhile, gains an effective boost on semantic segmentation of class imbalanced biomedical images." @default.
• W4294775257 created "2022-09-06" @default.
• W4294775257 creator A5019580006 @default.
• W4294775257 creator A5025515104 @default.
• W4294775257 creator A5048831651 @default.
• W4294775257 creator A5051729391 @default.
• W4294775257 creator A5063293419 @default.
• W4294775257 creator A5080263475 @default.
• W4294775257 creator A5083254525 @default.
• W4294775257 date "2022-11-01" @default.
• W4294775257 modified "2023-10-18" @default.
• W4294775257 title "Synthetic augmentation for semantic segmentation of class imbalanced biomedical images: A data pair generative adversarial network approach" @default.
• W4294775257 cites W1903029394 @default.
• W4294775257 cites W2119737461 @default.
• W4294775257 cites W2131006320 @default.
• W4294775257 cites W2147484997 @default.
• W4294775257 cites W2161237731 @default.
• W4294775257 cites W2171417304 @default.
• W4294775257 cites W2310992461 @default.
• W4294775257 cites W2593414223 @default.
• W4294775257 cites W2763355946 @default.
• W4294775257 cites W2789713147 @default.
• W4294775257 cites W2799738340 @default.
• W4294775257 cites W2806118840 @default.
• W4294775257 cites W2903141607 @default.
• W4294775257 cites W2921322750 @default.
• W4294775257 cites W2962770929 @default.
• W4294775257 cites W2962793481 @default.
• W4294775257 cites W2962974533 @default.
• W4294775257 cites W2963073614 @default.
• W4294775257 cites W2963942586 @default.
• W4294775257 cites W2964003427 @default.
• W4294775257 cites W2964227007 @default.
• W4294775257 cites W2996290406 @default.
• W4294775257 cites W3027427064 @default.
• W4294775257 cites W3035014113 @default.
• W4294775257 cites W3035037798 @default.
• W4294775257 cites W3035149916 @default.
• W4294775257 cites W3102785203 @default.
• W4294775257 cites W3104258355 @default.
• W4294775257 cites W3122244891 @default.
• W4294775257 cites W3132795731 @default.
• W4294775257 cites W3210085249 @default.
• W4294775257 cites W3211121045 @default.
• W4294775257 cites W3213666303 @default.
• W4294775257 cites W3214474910 @default.
• W4294775257 cites W3215208047 @default.
• W4294775257 cites W3215453560 @default.
• W4294775257 cites W4225572800 @default.
• W4294775257 doi "https://doi.org/10.1016/j.compbiomed.2022.105985" @default.
• W4294775257 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36137319" @default.
• W4294775257 hasPublicationYear "2022" @default.
• W4294775257 type Work @default.
• W4294775257 citedByCount "1" @default.
• W4294775257 countsByYear W42947752572022 @default.
• W4294775257 crossrefType "journal-article" @default.
• W4294775257 hasAuthorship W4294775257A5019580006 @default.
• W4294775257 hasAuthorship W4294775257A5025515104 @default.
• W4294775257 hasAuthorship W4294775257A5048831651 @default.
• W4294775257 hasAuthorship W4294775257A5051729391 @default.
• W4294775257 hasAuthorship W4294775257A5063293419 @default.
• W4294775257 hasAuthorship W4294775257A5080263475 @default.
• W4294775257 hasAuthorship W4294775257A5083254525 @default.
• W4294775257 hasConcept C119857082 @default.
• W4294775257 hasConcept C124504099 @default.
• W4294775257 hasConcept C153180895 @default.
• W4294775257 hasConcept C154945302 @default.
• W4294775257 hasConcept C2779803651 @default.
• W4294775257 hasConcept C41008148 @default.
• W4294775257 hasConcept C58489278 @default.
• W4294775257 hasConcept C76155785 @default.
• W4294775257 hasConcept C89600930 @default.
• W4294775257 hasConcept C94915269 @default.
• W4294775257 hasConceptScore W4294775257C119857082 @default.
• W4294775257 hasConceptScore W4294775257C124504099 @default.
• W4294775257 hasConceptScore W4294775257C153180895 @default.
• W4294775257 hasConceptScore W4294775257C154945302 @default.
• W4294775257 hasConceptScore W4294775257C2779803651 @default.
• W4294775257 hasConceptScore W4294775257C41008148 @default.
• W4294775257 hasConceptScore W4294775257C58489278 @default.
• W4294775257 hasConceptScore W4294775257C76155785 @default.
• W4294775257 hasConceptScore W4294775257C89600930 @default.
• W4294775257 hasConceptScore W4294775257C94915269 @default.
• W4294775257 hasLocation W42947752571 @default.
• W4294775257 hasLocation W42947752572 @default.
• W4294775257 hasOpenAccess W4294775257 @default.
• W4294775257 hasPrimaryLocation W42947752571 @default.
• W4294775257 hasRelatedWork W1489300767 @default.
• W4294775257 hasRelatedWork W1522196789 @default.
• W4294775257 hasRelatedWork W1980965563 @default.
• W4294775257 hasRelatedWork W2057775761 @default.
• W4294775257 hasRelatedWork W2089959425 @default.
• W4294775257 hasRelatedWork W2387995142 @default.
• W4294775257 hasRelatedWork W2964074194 @default.
• W4294775257 hasRelatedWork W4293202849 @default.
• W4294775257 hasRelatedWork W4319453655 @default.
• W4294775257 hasRelatedWork W4380714744 @default.
• W4294775257 hasVolume "150" @default.

• next