  Thyroid cancer is currently the fifth most common malignancy diagnosed in
women. Since differentiation of cancer sub-types is important for treatment and
current, manual methods are time consuming and subjective, automatic
computer-aided differentiation of cancer types is crucial. Manual
differentiation of thyroid cancer is based on tissue sections, analysed by
pathologists using histological features. Due to the enormous size of gigapixel
whole slide images, holistic classification using deep learning methods is not
feasible. Patch based multiple instance learning approaches, combined with
aggregations such as bag-of-words, is a common approach. This work's
contribution is to extend a patch based state-of-the-art method by generating
and combining feature vectors of three different patch resolutions and
analysing three distinct ways of combining them. The results showed
improvements in one of the three multi-scale approaches, while the others led
to decreased scores. This provides motivation for analysis and discussion of
the individual approaches.
