%0 Conference Proceedings
%T Inferring the Performance of Medical Imaging Algorithms
%A Aura Hernandez-Sabate
%A Debora Gil
%A David Roche
%A Monica M. S. Matsumoto
%A Sergio S. Furuie
%E Pedro Real
%E Daniel Diaz-Pernil
%E Helena Molina-Abril
%E Ainhoa Berciano
%E Walter Kropatsch
%B 14th International Conference on Computer Analysis of Images and Patterns
%D 2011
%V 6854
%I Springer-Verlag Berlin Heidelberg
%C Berlin
%F Aura Hernandez-Sabate2011
%O IAM; ADAS
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=1676), last updated on Fri, 18 Jul 2014 10:03:59 +0200
%X Evaluation of the performance and limitations of medical imaging algorithms is essential to estimate their impact in social, economic or clinical aspects. However, validation of medical imaging techniques is a challenging task due to the variety of imaging and clinical problems involved, as well as, the difficulties for systematically extracting a reliable solely ground truth. Although specific validation protocols are reported in any medical imaging paper, there are still two major concerns: definition of standardized methodologies transversal to all problems and generalization of conclusions to the whole clinical data set.We claim that both issues would be fully solved if we had a statistical model relating ground truth and the output of computational imaging techniques. Such a statistical model could conclude to what extent the algorithm behaves like the ground truth from the analysis of a sampling of the validation data set. We present a statistical inference framework reporting the agreement and describing the relationship of two quantities. We show its transversality by applying it to validation of two different tasks: contour segmentation and landmark correspondence.
%K Validation
%K Statistical Inference
%K Medical Imaging Algorithms.
%U http://congreso.us.es/caip2011/poster2.php
%U http://refbase.cvc.uab.es/files/HGR2011.pdf
%P 520-528