TY  - CONF
AU  - Aura Hernandez-Sabate
AU  - Debora Gil
AU  - David Roche
AU  - Monica M. S. Matsumoto
AU  - Sergio S. Furuie
A2  - CAIP
ED  - Pedro Real
ED  - Daniel Diaz-Pernil
ED  - Helena Molina-Abril
ED  - Ainhoa Berciano
ED  - Walter Kropatsch
PY  - 2011//
TI  - Inferring the Performance of Medical Imaging Algorithms
T2  - LNCS
BT  - 14th International Conference on Computer Analysis of Images and Patterns
T3  - L
SP  - 520
EP  - 528
VL  - 6854
PB  - Springer-Verlag Berlin Heidelberg
CY  - Berlin
KW  - Validation
KW  - Statistical Inference
KW  - Medical Imaging Algorithms.
N2  - 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.
UR  - http://congreso.us.es/caip2011/poster2.php
L1  - http://refbase.cvc.uab.es/files/HGR2011.pdf
N1  - IAM; ADAS
ID  - Aura Hernandez-Sabate2011
ER  -