  Despite the progress made in the field of medical imaging, it remains a large
area of open research, especially due to the variety of imaging modalities and
disease-specific characteristics. This paper is a comparative study describing
the potential of using local binary patterns (LBP), deep features and the
bag-of-visual words (BoVW) scheme for the classification of histopathological
images. We introduce a new dataset, \emph{KIMIA Path960}, that contains 960
histopathology images belonging to 20 different classes (different tissue
types). We make this dataset publicly available. The small size of the dataset
and its inter- and intra-class variability makes it ideal for initial
investigations when comparing image descriptors for search and classification
in complex medical imaging cases like histopathology. We investigate deep
features, LBP histograms and BoVW to classify the images via leave-one-out
validation. The accuracy of image classification obtained using LBP was 90.62\%
while the highest accuracy using deep features reached 94.72\%. The dictionary
approach (BoVW) achieved 96.50\%. Deep solutions may be able to deliver higher
accuracies but they need extensive training with a large number of (balanced)
image datasets.
