  In this work, we introduce a spatially transformed DenseNet architecture for
transformation invariant classification of cancer tissue. Our architecture
increases the accuracy of the base DenseNet architecture while adding the
ability to operate in a transformation invariant way while simultaneously being
simpler than other models that try to provide some form of invariance.
