  For classifying digital whole slide images in the absence of pixel level
annotation, typically multiple instance learning methods are applied. Due to
the generic applicability, such methods are currently of very high interest in
the research community, however, the issue of data augmentation in this context
is rarely explored. Here we investigate linear and multilinear interpolation
between feature vectors, a data augmentation technique, which proved to be
capable of improving the generalization performance classification networks and
also for multiple instance learning. Experiments, however, have been performed
on only two rather small data sets and one specific feature extraction approach
so far and a strong dependence on the data set has been identified. Here we
conduct a large study incorporating 10 different data set configurations, two
different feature extraction approaches (supervised and self-supervised), stain
normalization and two multiple instance learning architectures. The results
showed an extraordinarily high variability in the effect of the method. We
identified several interesting aspects to bring light into the darkness and
identified novel promising fields of research.
