  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.
