Resultant objective metrics of fused MR T1-MR T2 of proposed model in comparison with for various existing fusion algorithms.
<p>Resultant objective metrics of fused MR T1-MR T2 of proposed model in comparison with for various existing fusion algorithms.</p>
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2024
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| _version_ | 1852025741642825728 |
|---|---|
| author | Venu Allapakam (19935102) |
| author2 | Yepuganti Karuna (19935105) |
| author2_role | author |
| author_facet | Venu Allapakam (19935102) Yepuganti Karuna (19935105) |
| author_role | author |
| dc.creator.none.fl_str_mv | Venu Allapakam (19935102) Yepuganti Karuna (19935105) |
| dc.date.none.fl_str_mv | 2024-10-23T17:29:02Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0309651.t005 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Resultant_objective_metrics_of_fused_MR_T1-MR_T2_of_proposed_model_in_comparison_with_for_various_existing_fusion_algorithms_/27286131 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Medicine Cell Biology Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified weight map computing notably improved contrast literature like pca fixed fusion strategy existing fusion methods stacking ensemble method siamese neural networks numerous clinical applications machine learning approaches combine complementary information multimodality image fusion image fusion challenges proposed model performance modality medical images high visual quality medical image fusion 19 </ p visual quality ensemble model image quality fusion datasets source images practical approaches performance metrics numerous combinations detailed information model leveraging image modalities work proposes various combinations usually resulting unique capabilities trained networks publicly available many multi increased resolution effectively preserve complex procedures |
| dc.title.none.fl_str_mv | Resultant objective metrics of fused MR T1-MR T2 of proposed model in comparison with for various existing fusion algorithms. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>Resultant objective metrics of fused MR T1-MR T2 of proposed model in comparison with for various existing fusion algorithms.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_4d60dda565ca1d70137e1ee627dfa68a |
| identifier_str_mv | 10.1371/journal.pone.0309651.t005 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/27286131 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Resultant objective metrics of fused MR T1-MR T2 of proposed model in comparison with for various existing fusion algorithms.Venu Allapakam (19935102)Yepuganti Karuna (19935105)MedicineCell BiologyScience PolicySpace ScienceBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedweight map computingnotably improved contrastliterature like pcafixed fusion strategyexisting fusion methodsstacking ensemble methodsiamese neural networksnumerous clinical applicationsmachine learning approachescombine complementary informationmultimodality image fusionimage fusion challengesproposed model performancemodality medical imageshigh visual qualitymedical image fusion19 </ pvisual qualityensemble modelimage qualityfusion datasetssource imagespractical approachesperformance metricsnumerous combinationsdetailed informationmodel leveragingimage modalitieswork proposesvarious combinationsusually resultingunique capabilitiestrained networkspublicly availablemany multiincreased resolutioneffectively preservecomplex procedures<p>Resultant objective metrics of fused MR T1-MR T2 of proposed model in comparison with for various existing fusion algorithms.</p>2024-10-23T17:29:02ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0309651.t005https://figshare.com/articles/dataset/Resultant_objective_metrics_of_fused_MR_T1-MR_T2_of_proposed_model_in_comparison_with_for_various_existing_fusion_algorithms_/27286131CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/272861312024-10-23T17:29:02Z |
| spellingShingle | Resultant objective metrics of fused MR T1-MR T2 of proposed model in comparison with for various existing fusion algorithms. Venu Allapakam (19935102) Medicine Cell Biology Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified weight map computing notably improved contrast literature like pca fixed fusion strategy existing fusion methods stacking ensemble method siamese neural networks numerous clinical applications machine learning approaches combine complementary information multimodality image fusion image fusion challenges proposed model performance modality medical images high visual quality medical image fusion 19 </ p visual quality ensemble model image quality fusion datasets source images practical approaches performance metrics numerous combinations detailed information model leveraging image modalities work proposes various combinations usually resulting unique capabilities trained networks publicly available many multi increased resolution effectively preserve complex procedures |
| status_str | publishedVersion |
| title | Resultant objective metrics of fused MR T1-MR T2 of proposed model in comparison with for various existing fusion algorithms. |
| title_full | Resultant objective metrics of fused MR T1-MR T2 of proposed model in comparison with for various existing fusion algorithms. |
| title_fullStr | Resultant objective metrics of fused MR T1-MR T2 of proposed model in comparison with for various existing fusion algorithms. |
| title_full_unstemmed | Resultant objective metrics of fused MR T1-MR T2 of proposed model in comparison with for various existing fusion algorithms. |
| title_short | Resultant objective metrics of fused MR T1-MR T2 of proposed model in comparison with for various existing fusion algorithms. |
| title_sort | Resultant objective metrics of fused MR T1-MR T2 of proposed model in comparison with for various existing fusion algorithms. |
| topic | Medicine Cell Biology Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified weight map computing notably improved contrast literature like pca fixed fusion strategy existing fusion methods stacking ensemble method siamese neural networks numerous clinical applications machine learning approaches combine complementary information multimodality image fusion image fusion challenges proposed model performance modality medical images high visual quality medical image fusion 19 </ p visual quality ensemble model image quality fusion datasets source images practical approaches performance metrics numerous combinations detailed information model leveraging image modalities work proposes various combinations usually resulting unique capabilities trained networks publicly available many multi increased resolution effectively preserve complex procedures |