Paper Info
Title
Systematic assessment of performance prediction techniques in medical image classification: a case study on celiac disease.
Abstract
In the context of automated classification of medical images, many authors report a lack of available test data. Therefore techniques such as the leave-one-out cross validation or k-fold validation are used to assess how well methods will perform in practice. In case of methods based on feature subset selection, cross validation might provide bad estimations of how well the optimized technique generalizes on an independent data set. In this work, we assess how well cross validation techniques are suited to predict the outcome of a preferred setup of distinct test- and training data sets. This is accomplished by creating two distinct sets of images, used separately as training- and test-data. The experiments are conducted using a set of Local Binary Pattern based operators for feature extraction which are using histogram subset selection to improve the feature discrimination. Common problems such as the effects of over fitting data during cross validation as well as using biased image sets due to multiple images from a single patient are considered.
Year
DOI
Venue
2011
10.1007/978-3-642-22092-0_41
IPMI
Keywords
Field
DocType
fitting data,celiac disease,systematic assessment,medical image classification,performance prediction technique,k-fold validation,independent data,available test data,leave-one-out cross validation,case study,feature extraction,validation technique,cross validation,feature discrimination,training data set
Data mining,Histogram,Pattern recognition,Computer science,Local binary patterns,Feature extraction,Artificial intelligence,Test data,Overfitting,Contextual image classification,Cross-validation,Performance prediction
Conference
Volume
ISSN
Citations 
22
1011-2499
5
PageRank 
References 
Authors
0.47
6
3
Name
Order
Citations
PageRank
Sebastian Hegenbart1867.10
Andreas Uhl21958223.07
Andreas Vécsei316718.36