H-score is a semi-quantitative method used to assess the presence and distribution of proteins in tissue samples by combining the intensity of staining and percentage of stained nuclei. It is widely used but time-consuming and can be limited in accuracy and precision. Computer-aided methods may help overcome these limitations and improve the efficiency of pathologists' workflows. In this work, we developed a model EndoNet for automatic calculation of H-score on histological slides. Our proposed method uses neural networks and consists of two main parts. The first is a detection model which predicts keypoints of centers of nuclei. The second is a H-score module which calculates the value of the H-score using mean pixel values of predicted keypoints. Our model was trained and validated on 1780 annotated tiles with a shape of 100x100 $\mu m$ and performed 0.77 mAP on a test dataset. Moreover, the model can be adjusted to a specific specialist or whole laboratory to reproduce the manner of calculating the H-score. Thus, EndoNet is effective and robust in the analysis of histology slides, which can improve and significantly accelerate the work of pathologists.

翻译：H评分是一种半定量方法，通过结合染色强度与染色细胞核百分比来评估组织样本中蛋白质的存在及分布情况。该方法应用广泛，但耗时且精度与准确度受限。计算机辅助方法有助于克服这些局限性，提高病理学家的工作效率。本研究开发了EndoNet模型，用于组织学切片H评分的自动计算。所提方法基于神经网络，由两个主要部分组成：第一部分是检测模型，用于预测细胞核中心的关键点；第二部分是H评分模块，通过预测关键点的平均像素值计算H评分值。模型在1780个标注为100x100 μm的图块上训练并验证，在测试数据集上取得了0.77 mAP的性能。此外，该模型可根据特定专家或整个实验室的需求进行调整，以复现其H评分计算方式。因此，EndoNet在组织学切片分析中有效且稳健，能够改进并显著加快病理学家的工作效率。