A Quantitative Analysis Platform for PD-L1 Immunohistochemistry based on Point-level Supervision Model

A Quantitative Analysis Platform for PD-L1 Immunohistochemistry based on Point-level Supervision Model

Haibo Mi, Kele Xu, Yang Xiang, Yulin He, Dawei Feng, Huaimin Wang, Chun Wu, Yanming Song, Xiaolei Sun

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

Recently, deep learning has witnessed dramatic progress in the medical image analysis field. In the precise treatment of cancer immunotherapy, the quantitative analysis of PD-L1 immunohistochemistry is of great importance. It is quite common that pathologists manually quantify the cell nuclei. This process is very time-consuming and error-prone. In this paper, we describe the development of a platform for PD-L1 pathological image quantitative analysis using deep learning approaches. As point-level annotations can provide a rough estimate of the object locations and classifications, this platform adopts a point-level supervision model to classify, localize, and count the PD-L1 cells nuclei. Presently, this platform has achieved an accurate quantitative analysis of PD-L1 for two types of carcinoma, and it is deployed in one of the first-class hospitals in China.
Keywords:
AI: Computer Vision
Applications: Medical and healthcare