Abstract
Digital staining for the automated annotation of
mass spectrometry imaging (MSI) data has previously been
achieved using state-of-the-art classifiers such as random
forests or support vector machines (SVMs). However, the
training of such classifiers requires an expert to label exemplary
data in advance. This process is time-consuming and hence
costly, especially if the tissue is heterogeneous. In theory, it
may be sufficient to only label a few highly representative
pixels of an MS image, but it is not known a priori which pixels
to select. This motivates active learning strategies in which the
algorithm itself queries the expert by automatically suggesting promising candidate pixels of an MS image for labeling. Given a
suitable querying strategy, the number of required training labels can be significantly reduced while maintaining classification
accuracy. In this work, we propose active learning for convenient annotation of MSI data. We generalize a recently proposed
active learning method to the multiclass case and combine it with the random forest classifier. Its superior performance over
random sampling is demonstrated on secondary ion mass spectrometry data, making it an interesting approach for the
classification of MS images.
Original language | English |
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Pages (from-to) | 147-155 |
Number of pages | 9 |
Journal | Analytical Chemistry |
Volume | 85 |
DOIs | |
Publication status | Published - 2013 |