  Accurate subtyping of renal cell carcinoma (RCC) is of crucial importance for
understanding disease progression and for making informed treatment decisions.
New discoveries of significant alterations to mitochondria between subtypes
make immunohistochemical (IHC) staining based image classification an
imperative. Until now, accurate quantification and subtyping was made
impossible by huge IHC variations, the absence of cell membrane staining for
cytoplasm segmentation as well as the complete lack of systems for robust and
reproducible image based classification. In this paper we present a
comprehensive classification framework to overcome these challenges for tissue
microarrays (TMA) of RCCs. We compare and evaluate models based on domain
specific hand-crafted "flat"-features versus "deep" feature representations
from various layers of a pre-trained convolutional neural network (CNN). The
best model reaches a cross-validation accuracy of 89%, which demonstrates for
the first time, that robust mitochondria-based subtyping of renal cancer is
feasible
