EndoNet: model for automatic calculation of H-score on histological slides

TL;DR

EndoNet is an automated neural network-based tool designed to accurately and efficiently calculate H-scores from histological slides, aiding pathologists by reducing manual effort and increasing consistency.

Contribution

This work introduces EndoNet, a novel neural network model that automates H-score calculation, combining nuclei detection and pixel analysis for improved histological assessment.

Findings

Achieved 0.77 mAP on test dataset

Effective in reproducing H-score calculations across different users

Significantly accelerates histological slide analysis

Abstract

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.