The analysis and interpretation of histopathological samples and images is an important discipline in the diagnosis of
various diseases, especially cancer. An important factor in prognosis and treatment with the aim of a precision medicine
is the determination of so-called cancer stem cells (CSC) which are known for their resistance to chemotherapeutic
treatment and involvement in tumor recurrence. Using immunohistochemistry with CSC markers like CD13, CD133 and
others is one way to identify CSC. In our work we aim at identifying CSC presence on ubiquitous Hematoxilyn and Eosin
(HE) staining as an inexpensive tool for routine histopathology based on their distinct morphological features.
We present initial results of a new method based on color deconvolution (CD) and convolutional neural networks
(CNN). This method performs favorably (accuracy 0.936) in comparison with a state-of-the-art method based on 1DSIFT
and eigen-analysis feature sets evaluated on the same image database. We also show that accuracy of the CNN is
improved by the CD pre-processing.