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Breast cancer is one of the most common cancer in women around the world. Fordiagnosis, pathologists evaluate biomarkers such as HER2 protein usingimmunohistochemistry over tissue extracted by a biopsy. Through microscopicinspection, this assessment estimates the intensity and integrity of themembrane cells' staining and scores the sample as 0, 1+, 2+, or 3+: asubjective decision that depends on the interpretation of the pathologist. Thispaper presents the preliminary data analysis of the annotations of threepathologists over the same set of samples obtained using 20x magnification andincluding $1,252$ non-overlapping biopsy patches. We evaluate the intra- andinter-expert variability achieving substantial and moderate agreement,respectively, according to Fleiss' Kappa coefficient, as a previous stagetowards a generation of a HER2 breast cancer biopsy gold-standard usingsupervised learning from multiple pathologist annotations.

Gold-standard of HER2 breast cancer biopsies using supervised learning based on multiple pathologist annotations