Paper Info
Title
Incorporating human knowledge in automated celiac disease diagnosis
Abstract
Recently, computer-aided celiac disease diagnosis has been promoted to provide an objective opinion besides histological examination of biopsies and visual assessment of macroscopic mucosal tissue. State-of-the-art techniques, however, are not accurate enough to provide incentive for clinical deployment. In this work, we answer two questions: Do computers and human experts make similar classification errors and can expert knowledge be utilized to increase the accuracy of computer-aided methods. Three experts were asked to perform visual classification of a large number of images. The experts decisions were combined with nine different state-of-the-art image representations. Experimentation showed that the correlations between two computer-based methods were higher than the correlations between an expert and a computer-based method. Furthermore, the inclusion of expert knowledge led to statistically significant (p <; 0.05) improvements in 69 out of 108 investigated settings.
Year
DOI
Venue
2016
10.1109/IPTA.2016.7821009
2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA)
Keywords
Field
DocType
human knowledge,computer-aided celiac disease diagnosis,histological biopsy examination,visual assessment,macroscopic mucosal tissue,classification errors,expert knowledge,visual classification,state-of-the-art image representations
Computer vision,Disease,Software deployment,Incentive,Visualization,Medical imaging,Computer science,Visual assessment,Image representation,Human knowledge,Artificial intelligence,Machine learning
Conference
ISSN
ISBN
Citations 
2154-512X
978-1-4673-8911-2
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Michael Gadermayr17415.65
Hubert Kogler231.08
Maximilian Karla300.34
Andreas Vécsei416718.36
Andreas Uhl51958223.07
Dorit Merhof618955.02