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Leishmaniasis parasite segmentation and classification using deep learning

Author
Górriz, M.; Aparicio, A.; Raventós, B.; Vilaplana, V.; Sayrol, E.; Lopez, D.
Type of activity
Presentation of work at congresses
Name of edition
X Conference on Articulated Motion and Deformable Objects
Date of publication
2018
Presentation's date
2018-07-12
Book of congress proceedings
Articulated Motion and Deformable Objects 10th International Conference: AMDO 2018 Palma de Mallorca, Spain, July 12–13, 2018 Proceedings
First page
53
Last page
62
Publisher
Springer
DOI
https://doi.org/10.1007/978-3-319-94544-6_6 Open in new window
Project funding
Multimodal Signal Processing and Machine Learning on Graphs
Repository
http://hdl.handle.net/2117/126539 Open in new window
https://link.springer.com/chapter/10.1007/978-3-319-94544-6_6 Open in new window
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 t...
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.
Group of research
BIOCOM-SC - Computational Biology and Complex Systems Group
GPI - Image and Video Processing Group
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center

Participants