| 1. Identity statement | ||||
| Reference Type | Conference Paper (Conference Proceedings) | |||
| Site | sibgrapi.sid.inpe.br | |||
| Identifier | 8JMKD3MGPEW34M/49S63PP | |||
| Repository | sid.inpe.br/sibgrapi/2023/09.22.03.26 | |||
| Last Update | 2023:09.22.03.26.48 (UTC) giovana.a.benvenuto@unesp.br | |||
| Metadata Repository | sid.inpe.br/sibgrapi/2023/09.22.03.26.48 | |||
| Metadata Last Update | 2023:09.22.03.26.48 (UTC) giovana.a.benvenuto@unesp.br | |||
| Citation Key | BenvenutoCasa:2023:ReImRe | |||
| Title | Retinal images registration via unsupervised deep learning  | |||
| Format | On-line | |||
| Year | 2023 | |||
| Access Date | 2024, Apr. 28 | |||
| Number of Files | 1 | |||
| Size | 8236 KiB | |||
| 2. Context | ||||
| Author | 1 Benvenuto, Giovana Augusta 2 Casaca, Wallace | |||
| Affiliation | 1 UNESP 2 UNESP | |||
| Editor | Clua, Esteban Walter Gonzalez Körting, Thales Sehn Paulovich, Fernando Vieira Feris, Rogerio | |||
| e-Mail Address | giovana.a.benvenuto@unesp.br | |||
| Conference Name | Conference on Graphics, Patterns and Images, 36 (SIBGRAPI) | |||
| Conference Location | Rio Grande, RS | |||
| Date | Nov. 06-09, 2023 | |||
| Book Title | Proceedings | |||
| Tertiary Type | Master's or Doctoral Work | |||
| 3. Content and structure | ||||
| Is the master or a copy? | is the master | |||
| Content Stage | completed | |||
| Transferable | 1 | |||
| Keywords | Image registration image processing deep learning retina | |||
| Abstract | In ophthalmology and vision science applications, aligning a pair of retinal images is of paramount importance to support disease diagnosis and routine eye examinations. This paper introduces an end-to-end framework capable of learning the registration task in a fully unsupervised manner. The proposed approach combines Convolutional Neural Networks and Spatial Transformer Network into a unified pipeline that incorporates a similarity metric to gauge the difference between the images, enabling image alignment without requiring any ground-truth data. The validation study demonstrates that the model can successfully deal with several categories of fundus images, surpassing other recent techniques for retinal registration. | |||
| doc Directory Content | access | |||
| source Directory Content | there are no files | |||
| agreement Directory Content |
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| 4. Conditions of access and use | ||||
| data URL | http://urlib.net/ibi/8JMKD3MGPEW34M/49S63PP | |||
| zipped data URL | http://urlib.net/zip/8JMKD3MGPEW34M/49S63PP | |||
| Language | en | |||
| Target File | Benvenuto_CRWTD_Sibigrapi2023.pdf | |||
| User Group | giovana.a.benvenuto@unesp.br | |||
| Visibility | shown | |||
| 5. Allied materials | ||||
| Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 | |||
| Host Collection | sid.inpe.br/banon/2001/03.30.15.38 | |||
| 6. Notes | ||||
| Empty Fields | archivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition nexthigherunit notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume | |||
| 7. Description control | ||||
| e-Mail (login) | giovana.a.benvenuto@unesp.br | |||
| update | ||||