Automated Scoring System of HER2 in Pathological Images under the Microscope

TL;DR

This paper introduces an AI-based automated HER2 scoring system for pathological images that mimics real diagnostic workflows, improves accuracy, and assists pathologists by providing real-time analysis and visualization, facilitating faster and more reliable breast cancer diagnosis.

Contribution

The proposed system uniquely incorporates positive control for assay validation and offers real-time, visual HER2 scoring aligned with clinical guidelines, enhancing robustness and usability over previous methods.

Findings

Improves HER2 diagnostic accuracy in pathological images.

Provides real-time analysis and visualization for pathologists.

Enhances robustness and speed of HER2 diagnosis.

Abstract

Breast cancer is the most common cancer among women worldwide. The human epidermal growth factor receptor 2 (HER2) with immunohistochemical (IHC) is widely used for pathological evaluation to provide the appropriate therapy for patients with breast cancer. However, the deficiency of pathologists and subjective and susceptible to inter-observer variation of visual diagnosis are the main challenges. Recently, with the rapid development of artificial intelligence (AI) in disease diagnosis, several automated HER2 scoring methods using traditional computer vision or machine learning methods indicate the improvement of the HER2 diagnostic accuracy, but the unreasonable interpretation in pathology, as well as the expensive and ethical issues for annotation, make these methods still have a long way to deploy in hospitals to ease pathologists' burden in real. In this paper, we propose a HER2 automated scoring system that strictly follows the HER2 scoring guidelines simulating the real workflow of HER2 scores diagnosis by pathologists. Unlike the previous work, our method considers the positive control of HER2 to make sure the assay performance for each slide, eliminating work for repeated comparison between the current field of view (FOV) and positive control FOV, especially for the borderline cases. Besides, for each selected FOV under the microscope, our system provides real-time HER2 scores analysis and visualizations of the membrane staining intensity and completeness corresponding with the cell classifications. Our rigorous workflow along with the flexible interactive adjustion in demand substantially assists pathologists to finish the HER2 diagnosis faster and improves the robustness and accuracy. The proposed system will be embedded in our Thorough Eye platform for deployment in hospitals.