I worked on a similar pipeline for renal cell carcinoma a few years ago [1], although we only published a small subset of results since parts of the pipeline (e.g., finding representative tiles, survival prediction) had better results being produced elsewhere in the lab.

Regarding the hook in the headline -- computers surpassing pathologists -- it's a bit like automated driving in that even if true the immediate problem is the social and economic system. That is, we're not going to be removing the pathologist from the diagnostic and prognostic process anytime soon for many reasons, so how instead do we leverage machine learning in concert with the human observer to improve the diagnostic system? For that reason, decision introspection may be as valuable a topic of research as improving classification accuracy: justifying a particular automated classification to the pathologist, directing them to representative regions, and describing regions of feature space in biological terms.

[1] http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6945104