A Quantitative Analysis Platform for PD-L1 Immunohistochemistry based on Point-level Supervision Model
Haibo Mi, Kele Xu, Xiang Yang, Yulin He, Dawei Feng, Huaimin Wang, Chun Wu, Yanming Song, Xiaolei Sun · 2019
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.