The process of standard convolution and depthwise separable convolution.
<p>The process of standard convolution and depthwise separable convolution.</p>
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| Beste egile batzuk: | , , , , |
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2025
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Etiketa erantsi
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| _version_ | 1849927627472633856 |
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| author | Mehmet Zahid Genc (22683526) |
| author2 | Yaser Dalveren (22683529) Ali Kara (690960) Mohammad Derawi (22683532) Jan Kubicek (170285) Marek Penhaker (13014797) |
| author2_role | author author author author author |
| author_facet | Mehmet Zahid Genc (22683526) Yaser Dalveren (22683529) Ali Kara (690960) Mohammad Derawi (22683532) Jan Kubicek (170285) Marek Penhaker (13014797) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mehmet Zahid Genc (22683526) Yaser Dalveren (22683529) Ali Kara (690960) Mohammad Derawi (22683532) Jan Kubicek (170285) Marek Penhaker (13014797) |
| dc.date.none.fl_str_mv | 2025-11-25T18:35:18Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0332482.g002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/The_process_of_standard_convolution_and_depthwise_separable_convolution_/30714412 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Medicine Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified training compression techniques targeted architectural optimizations reliable imaging modalities promising results achieved neighborhood convolutional network model &# 8217 limited processing capabilities depthwise separable convolution high segmentation accuracy 3d organ segmentation routine clinical workflows memory demands make net builds upon entirely new architecture comparative analysis verified comparative analysis manual segmentation memory usage clinical deployment viable alternative time applications recent years purpose due ongoing efforts net variant net framework less suitable inference speed including upernet highly suitable first time findings contribute explore post edge devices constrained environments computed tomography 2d convolutions |
| dc.title.none.fl_str_mv | The process of standard convolution and depthwise separable convolution. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>The process of standard convolution and depthwise separable convolution.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_7ed11a2a167d16caae6a8f045cec496a |
| identifier_str_mv | 10.1371/journal.pone.0332482.g002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30714412 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | The process of standard convolution and depthwise separable convolution.Mehmet Zahid Genc (22683526)Yaser Dalveren (22683529)Ali Kara (690960)Mohammad Derawi (22683532)Jan Kubicek (170285)Marek Penhaker (13014797)MedicineSpace ScienceEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedtraining compression techniquestargeted architectural optimizationsreliable imaging modalitiespromising results achievedneighborhood convolutional networkmodel &# 8217limited processing capabilitiesdepthwise separable convolutionhigh segmentation accuracy3d organ segmentationroutine clinical workflowsmemory demands makenet builds uponentirely new architecturecomparative analysis verifiedcomparative analysismanual segmentationmemory usageclinical deploymentviable alternativetime applicationsrecent yearspurpose dueongoing effortsnet variantnet frameworkless suitableinference speedincluding upernethighly suitablefirst timefindings contributeexplore postedge devicesconstrained environmentscomputed tomography2d convolutions<p>The process of standard convolution and depthwise separable convolution.</p>2025-11-25T18:35:18ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0332482.g002https://figshare.com/articles/figure/The_process_of_standard_convolution_and_depthwise_separable_convolution_/30714412CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/307144122025-11-25T18:35:18Z |
| spellingShingle | The process of standard convolution and depthwise separable convolution. Mehmet Zahid Genc (22683526) Medicine Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified training compression techniques targeted architectural optimizations reliable imaging modalities promising results achieved neighborhood convolutional network model &# 8217 limited processing capabilities depthwise separable convolution high segmentation accuracy 3d organ segmentation routine clinical workflows memory demands make net builds upon entirely new architecture comparative analysis verified comparative analysis manual segmentation memory usage clinical deployment viable alternative time applications recent years purpose due ongoing efforts net variant net framework less suitable inference speed including upernet highly suitable first time findings contribute explore post edge devices constrained environments computed tomography 2d convolutions |
| status_str | publishedVersion |
| title | The process of standard convolution and depthwise separable convolution. |
| title_full | The process of standard convolution and depthwise separable convolution. |
| title_fullStr | The process of standard convolution and depthwise separable convolution. |
| title_full_unstemmed | The process of standard convolution and depthwise separable convolution. |
| title_short | The process of standard convolution and depthwise separable convolution. |
| title_sort | The process of standard convolution and depthwise separable convolution. |
| topic | Medicine Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified training compression techniques targeted architectural optimizations reliable imaging modalities promising results achieved neighborhood convolutional network model &# 8217 limited processing capabilities depthwise separable convolution high segmentation accuracy 3d organ segmentation routine clinical workflows memory demands make net builds upon entirely new architecture comparative analysis verified comparative analysis manual segmentation memory usage clinical deployment viable alternative time applications recent years purpose due ongoing efforts net variant net framework less suitable inference speed including upernet highly suitable first time findings contribute explore post edge devices constrained environments computed tomography 2d convolutions |