A Quantitative Analysis Platform for PD-L1 Immunohistochemistry Based on Point-Level Supervision Model
Abstract
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.
Cite
Text
Mi et al. "A Quantitative Analysis Platform for PD-L1 Immunohistochemistry Based on Point-Level Supervision Model." International Joint Conference on Artificial Intelligence, 2019. doi:10.24963/IJCAI.2019/954Markdown
[Mi et al. "A Quantitative Analysis Platform for PD-L1 Immunohistochemistry Based on Point-Level Supervision Model." International Joint Conference on Artificial Intelligence, 2019.](https://mlanthology.org/ijcai/2019/mi2019ijcai-quantitative/) doi:10.24963/IJCAI.2019/954BibTeX
@inproceedings{mi2019ijcai-quantitative,
title = {{A Quantitative Analysis Platform for PD-L1 Immunohistochemistry Based on Point-Level Supervision Model}},
author = {Mi, Haibo and Xu, Kele and Xiang, Yang and He, Yulin and Feng, Dawei and Wang, Huaimin and Wu, Chun and Song, Yanming and Sun, Xiaolei},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2019},
pages = {6554-6556},
doi = {10.24963/IJCAI.2019/954},
url = {https://mlanthology.org/ijcai/2019/mi2019ijcai-quantitative/}
}