Leishmaniasis parasite segmentation and classification using deep learning

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To access the full text documents, please follow this link: http://hdl.handle.net/2117/126539

dc.contributor	Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.contributor	Universitat Politècnica de Catalunya. Departament de Física
dc.contributor	Universitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo
dc.contributor	Universitat Politècnica de Catalunya. BIOCOM-SC - Grup de Biologia Computacional i Sistemes Complexos
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.date	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.citation	978-3-319-94543-9
dc.identifier.citation	https://link.springer.com/chapter/10.1007/978-3-319-94544-6_6
dc.identifier.citation	10.1007/978-3-319-94544-6_6
dc.identifier.uri	http://hdl.handle.net/2117/126539
dc.language.iso	eng
dc.publisher	Springer
dc.relation	info:eu-repo/grantAgreement/ES/2PE/ TEC2016-75976-R
dc.rights	info:eu-repo/semantics/openAccess
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	Deep learning
dc.subject	Leishmaniasis
dc.subject	Image segmentation
dc.subject	Aprenentatge profund
dc.subject	Leishmaniosi
dc.subject	Imatges -- Segmentació
dc.title	Leishmaniasis parasite segmentation and classification using deep learning
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	info:eu-repo/semantics/conferenceObject
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.description.abstract	Peer Reviewed

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