Abstract：
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.
摘要：
人表皮生长因子受体2（HER2）表达的评估是乳腺癌治疗选择的重要预后生物标志物。然而,由于中心之间的染色变化和需要在视觉上估计肿瘤面积的特定百分比的染色强度,HER2评分具有众所周知的高观察者间变异性。在本文中,关注病理学家对HER2评分的可解释性，我们提出了一个半自动的，两阶段深度学习方法，直接评估由美国临床肿瘤学会/美国病理学家学院（ASCO/CAP）定义的临床HER2指南。在第一阶段,我们在用户指示的感兴趣区域（ROI）上分割浸润性肿瘤。然后，在第二阶段,我们将肿瘤组织分为四类HER2.对于分类阶段，我们使用弱监督，约束优化，以找到对癌斑进行分类的模型，以使肿瘤表面百分比符合每个HER2类别的指南规范。我们通过冻结模型并以有监督的方式将其输出日志细化到训练集中的所有幻灯片标签来结束第二阶段。为了确保我们的数据集标签的质量，我们进行了多病理学家HER2评分共识.为了评估未达成共识的可疑案件，我们的模型可以通过解释其HER2类百分比输出来提供帮助.我们在测试集上的F1分数达到0.78的性能，同时保持我们的模型可为病理学家解释，希望有助于数字病理学中可解释的人工智能模型。
