dc.creator | Civit Masot, Javier | es |
dc.creator | Billis, Antonis | es |
dc.creator | Domínguez Morales, Manuel Jesús | es |
dc.creator | Vicente Díaz, Saturnino | es |
dc.creator | Civit Balcells, Antón | es |
dc.date.accessioned | 2022-07-20T09:32:58Z | |
dc.date.available | 2022-07-20T09:32:58Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Civit Masot, J., Billis, A., Domínguez Morales, M.J., Vicente Díaz, S. y Civit Balcells, A. (2020). Multidataset Incremental Training for Optic Disc Segmentation. En EANN 2020: 21st International Conference on Engineering Applications of Neural Networks (365-376), Held OnLine: Springer. | |
dc.identifier.isbn | 978-3-030-48790-4 | es |
dc.identifier.issn | 2661-8141 | es |
dc.identifier.uri | https://hdl.handle.net/11441/135634 | |
dc.description.abstract | When convolutional neural networks are applied to image
segmentation results depend greatly on the data sets used to train the
networks. Cloud providers support multi GPU and TPU virtual machines
making the idea of cloud-based segmentation as service attractive. In this
paper we study the problem of building a segmentation service, where
images would come from different acquisition instruments, by training a
generalized U-Net with images from a single or several datasets. We also
study the possibility of training with a single instrument and perform
quick retrains when more data is available. As our example we perform
segmentation of Optic Disc in fundus images which is useful for glau coma diagnosis. We use two publicly available data sets (RIM-One V3,
DRISHTI) for individual, mixed or incremental training. We show that
multidataset or incremental training can produce results that are simi lar to those published by researchers who use the same dataset for both
training and validation. | es |
dc.format | application/pdf | es |
dc.format.extent | 12 | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | EANN 2020: 21st International Conference on Engineering Applications of Neural Networks (2020), pp. 365-376. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Deep learning | es |
dc.subject | Eye fundus image segmentation | es |
dc.subject | Multiple dataset training | es |
dc.subject | Incremental training | es |
dc.subject | Glaucoma | es |
dc.title | Multidataset Incremental Training for Optic Disc Segmentation | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores | es |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/978-3-030-48791-1_28 | es |
dc.identifier.doi | 10.1007/978-3-030-48791-1_28 | es |
dc.contributor.group | Universidad de Sevilla. TEP-108: Robótica y Tecnología de Computadores | es |
dc.publication.initialPage | 365 | es |
dc.publication.endPage | 376 | es |
dc.eventtitle | EANN 2020: 21st International Conference on Engineering Applications of Neural Networks | es |
dc.eventinstitution | Held OnLine | es |
dc.relation.publicationplace | Cham, Switzerland | es |