Comparison of the training and validation time on DS1.
<p>Comparison of the training and validation time on DS1.</p>
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2024
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| _version_ | 1852025150416879616 |
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| author | Dhirendra Prasad Yadav (20235097) |
| author2 | Bhisham Sharma (20235100) Julian L. Webber (20235103) Abolfazl Mehbodniya (11988747) Shivank Chauhan (20235106) |
| author2_role | author author author author |
| author_facet | Dhirendra Prasad Yadav (20235097) Bhisham Sharma (20235100) Julian L. Webber (20235103) Abolfazl Mehbodniya (11988747) Shivank Chauhan (20235106) |
| author_role | author |
| dc.creator.none.fl_str_mv | Dhirendra Prasad Yadav (20235097) Bhisham Sharma (20235100) Julian L. Webber (20235103) Abolfazl Mehbodniya (11988747) Shivank Chauhan (20235106) |
| dc.date.none.fl_str_mv | 2024-11-15T18:36:28Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0311080.t004 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Comparison_of_the_training_and_validation_time_on_DS1_/27769393 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biotechnology Ecology Cancer Mental Health Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified sorensen &# 8211 resunet ++, u improve segmentation capabilities facilitate symmetrical interaction dice coefficient ). demonstrates superior quantitative 84 %, 96 81 %, 96 27 %, 95 range spatial dependencies diagnose lung cancer spatial aware attention enhanced spatial attention edtnet achieved 96 based vision transformer based model edtnet spatial features provide attention lung lesions based transformer based encoders edtnet performance visual results union ), traditional cnn suboptimal performance small size skip connections several models scan images sampling layers intricate details expanding layer diverse nature demonstrated sensitivity capture long |
| dc.title.none.fl_str_mv | Comparison of the training and validation time on DS1. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>Comparison of the training and validation time on DS1.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_5d7bf06bff2a8dce2ff6bb425b3efebd |
| identifier_str_mv | 10.1371/journal.pone.0311080.t004 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/27769393 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Comparison of the training and validation time on DS1.Dhirendra Prasad Yadav (20235097)Bhisham Sharma (20235100)Julian L. Webber (20235103)Abolfazl Mehbodniya (11988747)Shivank Chauhan (20235106)BiotechnologyEcologyCancerMental HealthSpace ScienceEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedsorensen &# 8211resunet ++, uimprove segmentation capabilitiesfacilitate symmetrical interactiondice coefficient ).demonstrates superior quantitative84 %, 9681 %, 9627 %, 95range spatial dependenciesdiagnose lung cancerspatial aware attentionenhanced spatial attentionedtnet achieved 96based vision transformerbased model edtnetspatial featuresprovide attentionlung lesionsbased transformerbased encodersedtnet performancevisual resultsunion ),traditional cnnsuboptimal performancesmall sizeskip connectionsseveral modelsscan imagessampling layersintricate detailsexpanding layerdiverse naturedemonstrated sensitivitycapture long<p>Comparison of the training and validation time on DS1.</p>2024-11-15T18:36:28ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0311080.t004https://figshare.com/articles/dataset/Comparison_of_the_training_and_validation_time_on_DS1_/27769393CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/277693932024-11-15T18:36:28Z |
| spellingShingle | Comparison of the training and validation time on DS1. Dhirendra Prasad Yadav (20235097) Biotechnology Ecology Cancer Mental Health Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified sorensen &# 8211 resunet ++, u improve segmentation capabilities facilitate symmetrical interaction dice coefficient ). demonstrates superior quantitative 84 %, 96 81 %, 96 27 %, 95 range spatial dependencies diagnose lung cancer spatial aware attention enhanced spatial attention edtnet achieved 96 based vision transformer based model edtnet spatial features provide attention lung lesions based transformer based encoders edtnet performance visual results union ), traditional cnn suboptimal performance small size skip connections several models scan images sampling layers intricate details expanding layer diverse nature demonstrated sensitivity capture long |
| status_str | publishedVersion |
| title | Comparison of the training and validation time on DS1. |
| title_full | Comparison of the training and validation time on DS1. |
| title_fullStr | Comparison of the training and validation time on DS1. |
| title_full_unstemmed | Comparison of the training and validation time on DS1. |
| title_short | Comparison of the training and validation time on DS1. |
| title_sort | Comparison of the training and validation time on DS1. |
| topic | Biotechnology Ecology Cancer Mental Health Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified sorensen &# 8211 resunet ++, u improve segmentation capabilities facilitate symmetrical interaction dice coefficient ). demonstrates superior quantitative 84 %, 96 81 %, 96 27 %, 95 range spatial dependencies diagnose lung cancer spatial aware attention enhanced spatial attention edtnet achieved 96 based vision transformer based model edtnet spatial features provide attention lung lesions based transformer based encoders edtnet performance visual results union ), traditional cnn suboptimal performance small size skip connections several models scan images sampling layers intricate details expanding layer diverse nature demonstrated sensitivity capture long |