Incorporating Human Knowledge in Automated Celiac Disease Diagnosis

被引:0
|
作者
Gadermayr, Michael [1 ]
Kogler, Hubert [2 ]
Karla, Maximilian [2 ]
Vecsei, Andreas [2 ]
Uhl, Andreas [3 ]
Merhof, Dorit [1 ]
机构
[1] Rhein Westfal TH Aachen, Inst Imaging & Comp Vis, Aachen Ctr Biomed Image Anal Visualizat & Explora, Aachen, Germany
[2] Med Univ Vienna, St Anna Childrens Hosp, Dept Pediat, Vienna, Austria
[3] Salzburg Univ, Dept Comp Sci, Salzburg, Austria
来源
2016 SIXTH INTERNATIONAL CONFERENCE ON IMAGE PROCESSING THEORY, TOOLS AND APPLICATIONS (IPTA) | 2016年
关键词
PREVALENCE; CLASSIFICATION;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
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页数:6
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