  The evaluation of the Human Epidermal growth factor Receptor-2 (HER2)
expression is an important prognostic biomarker for breast cancer treatment
selection. However, HER2 scoring has notoriously high interobserver variability
due to stain variations between centers and the need to estimate visually the
staining intensity in specific percentages of tumor area. In this paper,
focusing on the interpretability of HER2 scoring by a pathologist, we propose a
semi-automatic, two-stage deep learning approach that directly evaluates the
clinical HER2 guidelines defined by the American Society of Clinical Oncology/
College of American Pathologists (ASCO/CAP). In the first stage, we segment the
invasive tumor over the user-indicated Region of Interest (ROI). Then, in the
second stage, we classify the tumor tissue into four HER2 classes. For the
classification stage, we use weakly supervised, constrained optimization to
find a model that classifies cancerous patches such that the tumor surface
percentage meets the guidelines specification of each HER2 class. We end the
second stage by freezing the model and refining its output logits in a
supervised way to all slide labels in the training set. To ensure the quality
of our dataset's labels, we conducted a multi-pathologist HER2 scoring
consensus. For the assessment of doubtful cases where no consensus was found,
our model can help by interpreting its HER2 class percentages output. We
achieve a performance of 0.78 in F1-score on the test set while keeping our
model interpretable for the pathologist, hopefully contributing to
interpretable AI models in digital pathology.
