Unsupervised deep learning network for deformable fundus image registration

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorBenvenuto, Giovana Augusta-
Autor(es): dc.creatorColnago, Marilaine-
Autor(es): dc.creatorCasaca, Wallace-
Data de aceite: dc.date.accessioned2025-08-21T23:24:27Z-
Data de disponibilização: dc.date.available2025-08-21T23:24:27Z-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2021-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/ICASSP43922.2022.9747686-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/240453-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/240453-
Descrição: dc.descriptionIn ophthalmology and vision science applications, the process of registering a pair of fundus images, captured at different scales and viewing angles, is of paramount importance to support the diagnosis of diseases and routine eye examinations. Aiming at addressing the retina registration problem from the Deep Learning perspective, in this paper we introduce an end-to-end framework capable of learning the registration task in a fully unsupervised way. The designed approach combines Convolutional Neural Networks and Spatial Transformation Network into a unified pipeline that takes a similarity metric to gauge the difference between the images, thus enabling the image alignment without requiring any ground-truth data. Once the model is fully trained, it can perform one-shot registrations by just providing as input the pair of fundus images. As shown in the validation study, the trained model is able to successfully deal with several categories of fundus images, surpassing other recent techniques for retina registration.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionSão Paulo State University Faculty of Science and Technology-
Descrição: dc.descriptionSão Paulo State University Department of Energy Engineering-
Descrição: dc.descriptionSão Paulo State University Faculty of Science and Technology-
Descrição: dc.descriptionSão Paulo State University Department of Energy Engineering-
Descrição: dc.descriptionFAPESP: #2013/07375-0-
Descrição: dc.descriptionFAPESP: #2019/26288-7-
Descrição: dc.descriptionFAPESP: #2021/03328-3-
Formato: dc.format1281-1285-
Formato: dc.formatapplication/pdf-
Idioma: dc.languageen-
Relação: dc.relationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectDeep learning-
Palavras-chave: dc.subjectFundus image registration-
Título: dc.titleUnsupervised deep learning network for deformable fundus image registration-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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