Hierarchical annotation of medical images
Journal
Pattern Recognition
Date Issued
2011-03-29
Author(s)
Kocev, Dragi
Djeroski, Sasho
Abstract
We present a hierarchical multi-label classification (HMC) system for medical image annotation.
HMC is a variant of classification where an instance may belong to multiple classes at the same
time and these classes/labels are organized in a hierarchy. Our approach to HMC exploits the annotation hierarchy by building a single predictive clustering tree (PCT) that can simultaneously
predict all annotations of an image. Hence, PCTs are very efficient: a single classifier is valid
for the hierarchical semantics as a whole, as compared to other approaches that produce many
classifiers, each valid just for one given class. To improve performance, we construct ensembles
of PCTs. We evaluate our system on the IRMA database that consists of X-ray images. We investigate its performance under a variety of conditions. To begin with, we consider two ensemble
approaches, bagging and random forests. Next, we use several state-of-the-art feature extraction
approaches and combinations thereof. Finally, we employ two types of feature fusion, i.e., lowand high-level fusion. The experiments show that our system outperforms the best-performing
approach from the literature (a collection of SVMs, each predicting one label at the lowest level
of the hierarchy), both in terms of error and efficiency. This holds across a range of descriptors
and descriptor combinations, regardless of the type of feature fusion used. To stress the generality of the proposed approach, we have also applied it for automatic annotation of a large number
of consumer photos with multiple annotations organized in semantic hierarchy. The obtained
results show that this approach is general and easily applicable in different domains, offering
state-of-the-art performance.
HMC is a variant of classification where an instance may belong to multiple classes at the same
time and these classes/labels are organized in a hierarchy. Our approach to HMC exploits the annotation hierarchy by building a single predictive clustering tree (PCT) that can simultaneously
predict all annotations of an image. Hence, PCTs are very efficient: a single classifier is valid
for the hierarchical semantics as a whole, as compared to other approaches that produce many
classifiers, each valid just for one given class. To improve performance, we construct ensembles
of PCTs. We evaluate our system on the IRMA database that consists of X-ray images. We investigate its performance under a variety of conditions. To begin with, we consider two ensemble
approaches, bagging and random forests. Next, we use several state-of-the-art feature extraction
approaches and combinations thereof. Finally, we employ two types of feature fusion, i.e., lowand high-level fusion. The experiments show that our system outperforms the best-performing
approach from the literature (a collection of SVMs, each predicting one label at the lowest level
of the hierarchy), both in terms of error and efficiency. This holds across a range of descriptors
and descriptor combinations, regardless of the type of feature fusion used. To stress the generality of the proposed approach, we have also applied it for automatic annotation of a large number
of consumer photos with multiple annotations organized in semantic hierarchy. The obtained
results show that this approach is general and easily applicable in different domains, offering
state-of-the-art performance.
Subjects
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