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Building an Automated Cellular Annotation Framework

A Probabilistic Computer Vision Approach
BuchKartoniert, Paperback
108 Seiten
Englisch
LAP Lambert Academic Publishingerschienen am19.02.2014
Research in Leishmania and related parasites produces large amounts (up to the thousands) of microscopy images, which, in turn, require large amounts of time to classify and annotate. Not only does this detract the researchers from exploring new alternatives by robbing them of useful time, as it is also prone to inter-person variance. This lead us to the need for automatic or semi-automatic methods for image classification and annotation algorithms. In this book we guide the reader through the basic building blocks of a multi-layer computer vision / AI hybrid algorithm designed to automatically process confocal microscopy images in a consistent pipeline. In addition, a thorough validation of the algorithm is presented in a two-stage process. Firstly, by analysing each of its constituent components and secondly, by pitying it against a team of expert biomedical researchers to assess its real-world performance, as well as to reveal potential future improvements.mehr

Produkt

KlappentextResearch in Leishmania and related parasites produces large amounts (up to the thousands) of microscopy images, which, in turn, require large amounts of time to classify and annotate. Not only does this detract the researchers from exploring new alternatives by robbing them of useful time, as it is also prone to inter-person variance. This lead us to the need for automatic or semi-automatic methods for image classification and annotation algorithms. In this book we guide the reader through the basic building blocks of a multi-layer computer vision / AI hybrid algorithm designed to automatically process confocal microscopy images in a consistent pipeline. In addition, a thorough validation of the algorithm is presented in a two-stage process. Firstly, by analysing each of its constituent components and secondly, by pitying it against a team of expert biomedical researchers to assess its real-world performance, as well as to reveal potential future improvements.
Details
ISBN/GTIN978-3-659-51377-0
ProduktartBuch
EinbandartKartoniert, Paperback
Erscheinungsjahr2014
Erscheinungsdatum19.02.2014
Seiten108 Seiten
SpracheEnglisch
Gewicht160 g
Artikel-Nr.31367764

Autor

Pedro Nogueira holds a Masters degree in Networks and Informatics Systems Engineering from the University of Porto. He is a researcher and enthusiast on computer vision and machine learning methods, with several scientific publications on both domains. Pedro is currently an Assistant Professor and Ph.D. researcher at the University of Porto.
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Nogueira, Pedro