AKMI

Advanced Kernel-Methods for Medical Imaging

Coordinatore	KOBENHAVNS UNIVERSITET    more  Organization address postcode: 1017 contact info Titolo: Dr. Nome: Martin Cognome: Zachariasen Email: send email Telefono: +45 35321357
Nazionalità Coordinatore	Denmark [DK]
Totale costo	100˙000 €
EC contributo	100˙000 €
Programma	FP7-PEOPLE Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	FP7-PEOPLE-2011-CIG
Funding Scheme	MC-CIG
Anno di inizio	2012
Periodo (anno-mese-giorno)	2012-05-01   -   2016-04-30

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	KOBENHAVNS UNIVERSITET	DK	coordinator	100˙000.00

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 Obiettivo del progetto (Objective)

'The goal of this project is to develop kernel-based machine learning methods for image classification that employ similarity measures comparing images in a hierarchical fashion - as humans do, but with the accuracy of a computer. These methods shall allow to solve challenging medical imaging problems, in particular they will be applied to the diagnosis of osteoarthritis (OA) and breast cancer, which are ranked among the most burdening diseases.

Looking at the visual cortex, it becomes obvious that the human visual system uses `deep' structure consisting of multiple levels of processing operating on more and more abstract representations of the visual scene. This has been successfully copied in computer vision systems, In contrast, kernel-based learning algorithms such as support vector machine (SVM) classifiers mark the state-of-the art in pattern recognition. They employ (Mercer) kernel functions to implicitly define a metric feature space for processing the input data, that is, the kernel defines the similarity between observations, in our case between medical images. Kernel methods are well understood theoretically and give excellent results in practice. However, they are usually considered to be `shallow' learning methods in the sense that they realize only a single layer of non-linear processing. This project will combine hierarchical image processing with the efficiency, theoretical beauty, and accuracy gain of SVMs for advancing the performance of medical imaging systems. This is made possible by marrying the applicants expertise in kernel-based machine learning with the widely recognized knowledge in medical image analysis at his new affiliation The Image Group at the Department of Computer Science, University of Copenhagen (DIKU).'