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<identifier>oai:HAL:hal-00958909v1</identifier>
<datestamp>2018-01-11</datestamp>
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<title lang=en>Biomedical Images Classification by Universal Nearest Neighbours Classifier Using Posterior Probability</title>
<creator>D'Ambrosio, Roberto</creator>
<creator>Bel Haj Ali, Wafa</creator>
<creator>Nock, Richard</creator>
<creator>Soda, Paolo</creator>
<creator>Nielsen, Franck</creator>
<creator>Barlaud, Michel</creator>
<contributor>Medical Informatics and Computer Science Laboratory ; Università Campus Bio-Medico di Roma (UCBM)</contributor>
<contributor>Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Projet MEDIACODING ; Signal, Images et Systèmes (SIS) ; Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S) ; Université Nice Sophia Antipolis (UNS) ; Université Côte d'Azur (UCA) - Université Côte d'Azur (UCA) - Centre National de la Recherche Scientifique (CNRS) - Université Nice Sophia Antipolis (UNS) ; Université Côte d'Azur (UCA) - Université Côte d'Azur (UCA) - Centre National de la Recherche Scientifique (CNRS) - Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S) ; Université Nice Sophia Antipolis (UNS) ; Université Côte d'Azur (UCA) - Université Côte d'Azur (UCA) - Centre National de la Recherche Scientifique (CNRS) - Université Nice Sophia Antipolis (UNS) ; Université Côte d'Azur (UCA) - Université Côte d'Azur (UCA) - Centre National de la Recherche Scientifique (CNRS)</contributor>
<contributor>Centre de Recherche en Economie, Gestion, Modélisation et Informatique Appliquée (CEREGMIA) ; Université des Antilles et de la Guyane (UAG)</contributor>
<contributor>Sony Computer Science Laboratories [Tokyo, Japan] ; Sony</contributor>
<contributor>Institut Universitaire de France (IUF) ; Ministère de l'Éducation nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.)</contributor>
<description>Third International Workshop, MLMI 2012, Held in Conjunction with MICCAI 2012</description>
<description>International audience</description>
<source>Machine Learning in Medical Imaging</source>
<source>MICCAI - 15th International Conference on Medical Image Computing and Computer Assisted Intervention</source>
<coverage>Nice, France</coverage>
<publisher>Springer</publisher>
<identifier>hal-00958909</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00958909</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00958909/document</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00958909/file/Miccai.pdf</identifier>
<source>https://hal.archives-ouvertes.fr/hal-00958909</source>
<source>MICCAI - 15th International Conference on Medical Image Computing and Computer Assisted Intervention, Oct 2012, Nice, France. Springer, 7588, pp.119-127, 2012, Lecture Notes in Computer Science. 〈10.1007/978-3-642-35428-1_15〉</source>
<identifier>DOI : 10.1007/978-3-642-35428-1_15</identifier>
<relation>info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-642-35428-1_15</relation>
<language>en</language>
<subject lang=en>Image Processing and Computer Vision</subject>
<subject lang=en>Pattern Recognition</subject>
<subject lang=en>Artificial Intelligence (incl. Robotics)</subject>
<subject lang=en>Computer Imaging</subject>
<subject lang=en>Vision</subject>
<subject lang=en>Pattern Recognition and Graphics</subject>
<subject lang=en>Database Management</subject>
<subject lang=en>Computer Graphics</subject>
<subject>[INFO.INFO-IM] Computer Science [cs]/Medical Imaging</subject>
<subject>[SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging</subject>
<subject>[INFO.INFO-TI] Computer Science [cs]/Image Processing</subject>
<subject>[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]</subject>
<type>info:eu-repo/semantics/conferenceObject</type>
<type>Conference papers</type>
<description lang=en>Universal Nearest Neighbours (unn) is a classifier recently proposed, which can also effectively estimates the posterior probability of each classification act. This algorithm, intrinsically binary, requires the use of a decomposition method to cope with multiclass problems, thus reducing their complexity in less complex binary subtasks. Then, a reconstruction rule provides the final classification. In this paper we show that the application of unn algorithm in conjunction with a reconstruction rule based on the posterior probabilities provides a classification scheme robust among different biomedical image datasets. To this aim, we compare unn performance with those achieved by Support Vector Machine with two different kernels and by a k Nearest Neighbours classifier, and applying two different reconstruction rules for each of the aforementioned classification paradigms. The results on one private and five public biomedical datasets show satisfactory performance.</description>
<date>2012-10-01</date>
</dc>
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