The overexpression of human epidermal growth factor receptor 2 (HER2) has been established as a therapeutic target in multiple types of cancers, such as breast and gastric cancers. Immunohistochemistry (IHC) is employed as a basic HER2 test to identify the HER2-positive, borderline, and HER2-negative patients. However, the reliability and accuracy of HER2 scoring are affected by many factors, such as pathologists' experience. Recently, artificial intelligence (AI) has been used in various disease diagnosis to improve diagnostic accuracy and reliability, but the interpretation of diagnosis results is still an open problem. In this paper, we propose a real-time HER2 scoring system, which follows the HER2 scoring guidelines to complete the diagnosis, and thus each step is explainable. Unlike the previous scoring systems based on whole-slide imaging, our HER2 scoring system is integrated into an augmented reality (AR) microscope that can feedback AI results to the pathologists while reading the slide. The pathologists can help select informative fields of view (FOVs), avoiding the confounding regions, such as DCIS. Importantly, we illustrate the intermediate results with membrane staining condition and cell classification results, making it possible to evaluate the reliability of the diagnostic results. Also, we support the interactive modification of selecting regions-of-interest, making our system more flexible in clinical practice. The collaboration of AI and pathologists can significantly improve the robustness of our system. We evaluate our system with 285 breast IHC HER2 slides, and the classification accuracy of 95\% shows the effectiveness of our HER2 scoring system.

中文翻译：

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基于显微镜的 HER2 评分系统

人表皮生长因子受体 2 (HER2) 的过度表达已被确定为多种类型癌症（如乳腺癌和胃癌）的治疗靶点。免疫组织化学 (IHC) 被用作基本的 HER2 测试，以识别 HER2 阳性、临界和 HER2 阴性患者。然而，HER2 评分的可靠性和准确性受到许多因素的影响，例如病理学家的经验。近年来，人工智能（AI）已被用于各种疾病诊断，以提高诊断的准确性和可靠性，但诊断结果的解释仍然是一个悬而未决的问题。在本文中，我们提出了一种实时 HER2 评分系统，它遵循 HER2 评分指南来完成诊断，因此每个步骤都是可解释的。与之前基于全载玻片成像的评分系统不同，我们的 HER2 评分系统集成到增强现实 (AR) 显微镜中，该显微镜可以在阅读载玻片时将 AI 结果反馈给病理学家。病理学家可以帮助选择信息视野 (FOV)，避免混淆区域，例如 DCIS。重要的是，我们用膜染色条件和细胞分类结果说明了中间结果，从而可以评估诊断结果的可靠性。此外，我们支持选择感兴趣区域的交互式修改，使我们的系统在临床实践中更加灵活。AI 和病理学家的合作可以显着提高我们系统的稳健性。我们使用 285 个乳房 IHC HER2 载玻片评估我们的系统，