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Leishmaniasis parasite segmentation and classification using deep learning

Autor
Górriz, M.; Aparicio, A.; Raventós, B.; Vilaplana, V.; Sayrol, E.; Lopez, D.
Tipus d'activitat
Presentació treball a congrés
Nom de l'edició
X Conference on Articulated Motion and Deformable Objects
Any de l'edició
2018
Data de presentació
2018-07-12
Llibre d'actes
Articulated Motion and Deformable Objects 10th International Conference: AMDO 2018 Palma de Mallorca, Spain, July 12–13, 2018 Proceedings
Pàgina inicial
53
Pàgina final
62
Editor
Springer
DOI
https://doi.org/10.1007/978-3-319-94544-6_6 Obrir en finestra nova
Projecte finançador
Procesado de señales multimodales y aprendizaje automático en grafos.
Repositori
http://hdl.handle.net/2117/126539 Obrir en finestra nova
https://link.springer.com/chapter/10.1007/978-3-319-94544-6_6 Obrir en finestra nova
Resum
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 t...
Citació
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.
Grup de recerca
BIOCOM-SC - Grup de Biologia Computacional i Sistemes Complexos
GPI - Grup de Processament d'Imatge i Vídeo
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center

Participants