  Evaluating expression of the Human epidermal growth factor receptor 2 (Her2)
by visual examination of immunohistochemistry (IHC) on invasive breast cancer
(BCa) is a key part of the diagnostic assessment of BCa due to its recognised
importance as a predictive and prognostic marker in clinical practice. However,
visual scoring of Her2 is subjective and consequently prone to inter-observer
variability. Given the prognostic and therapeutic implications of Her2 scoring,
a more objective method is required. In this paper, we report on a recent
automated Her2 scoring contest, held in conjunction with the annual PathSoc
meeting held in Nottingham in June 2016, aimed at systematically comparing and
advancing the state-of-the-art Artificial Intelligence (AI) based automated
methods for Her2 scoring. The contest dataset comprised of digitised whole
slide images (WSI) of sections from 86 cases of invasive breast carcinoma
stained with both Haematoxylin & Eosin (H&E) and IHC for Her2. The contesting
algorithms automatically predicted scores of the IHC slides for an unseen
subset of the dataset and the predicted scores were compared with the 'ground
truth' (a consensus score from at least two experts). We also report on a
simple Man vs Machine contest for the scoring of Her2 and show that the
automated methods could beat the pathology experts on this contest dataset.
This paper presents a benchmark for comparing the performance of automated
algorithms for scoring of Her2. It also demonstrates the enormous potential of
automated algorithms in assisting the pathologist with objective IHC scoring.
