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Leishmaniasis parasite segmentation and classification using deep learning
dc.contributor.author | Górriz, Marc |
dc.contributor.author | Aparicio, Albert |
dc.contributor.author | Raventós, Berta |
dc.contributor.author | Vilaplana Besler, Verónica |
dc.contributor.author | Sayrol Clols, Elisa |
dc.contributor.author | López Codina, Daniel |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Física |
dc.date.accessioned | 2019-01-11T07:33:18Z |
dc.date.issued | 2018 |
dc.identifier.citation | Górriz, M., Aparicio, A., Raventós, B., Vilaplana, V., Sayrol, E., Lopez, D. Leishmaniasis parasite segmentation and classification using deep learning. A: Conference on Articulated Motion and Deformable Objects. "Articulated Motion and Deformable Objects 10th International Conference: AMDO 2018 Palma de Mallorca, Spain, July 12–13, 2018 Proceedings". Berlín: Springer, 2018, p. 53-62. |
dc.identifier.isbn | 978-3-319-94543-9 |
dc.identifier.other | https://link.springer.com/chapter/10.1007/978-3-319-94544-6_6 |
dc.identifier.uri | http://hdl.handle.net/2117/126539 |
dc.description.abstract | Leishmaniasis is considered a neglected disease that causes thousands of deaths annually in some tropical and subtropical countries. There are various techniques to diagnose leishmaniasis of which manual microscopy is considered to be the gold standard. There is a need for the development of automatic techniques that are able to detect parasites in a robust and unsupervised manner. In this paper we present a procedure for automatizing the detection process based on a deep learning approach. We train a U-net model that successfully segments leismania parasites and classifies them into promastigotes, amastigotes and adhered parasites. |
dc.format.extent | 10 p. |
dc.language.iso | eng |
dc.publisher | Springer |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
dc.subject | Àrees temàtiques de la UPC::Ciències de la salut::Medicina::Diagnòstic per la imatge |
dc.subject | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo |
dc.subject.lcsh | Deep learning |
dc.subject.lcsh | Leishmaniasis |
dc.subject.lcsh | Image segmentation |
dc.title | Leishmaniasis parasite segmentation and classification using deep learning |
dc.type | Conference lecture |
dc.subject.lemac | Aprenentatge profund |
dc.subject.lemac | Leishmaniosi |
dc.subject.lemac | Imatges -- Segmentació |
dc.contributor.group | Universitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo |
dc.contributor.group | Universitat Politècnica de Catalunya. BIOCOM-SC - Grup de Biologia Computacional i Sistemes Complexos |
dc.identifier.doi | 10.1007/978-3-319-94544-6_6 |
dc.description.peerreviewed | Peer Reviewed |
dc.rights.access | Restricted access - publisher's policy |
local.identifier.drac | 23572924 |
dc.description.version | Postprint (published version) |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO/2PE/TEC2016-75976-R |
dc.date.lift | 10000-01-01 |
local.citation.author | Górriz, M.; Aparicio, A.; Raventós, B.; Vilaplana, V.; Sayrol, E.; Lopez, D. |
local.citation.contributor | Conference on Articulated Motion and Deformable Objects |
local.citation.pubplace | Berlín |
local.citation.publicationName | Articulated Motion and Deformable Objects 10th International Conference: AMDO 2018 Palma de Mallorca, Spain, July 12–13, 2018 Proceedings |
local.citation.startingPage | 53 |
local.citation.endingPage | 62 |