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AACR 2023 Poster 6592

An AI Platform for Vascular and Fibrosis Analysis of Pathology Images

Yawen Zheng, Dawei Wang, Likun Zhang, Sheng Guo
Crown Bioscience, Inc., 218 Xinghu Street, Suzhou, Jiangsu, 215000, China

The efficacy of oncology drugs is affected by blood vessel density and cancer-associated fibrosis within a tumor microenvironment (TME). Traditional human visual immunohistochemical (IHC) scoring methods are time-consuming, inaccurate, and biased due to partial analysis of pathology images. To overcome these challenges, our online AI image analysis platform integrates machine learning algorithms in Computer Vision, enabling accurate and comprehensive analysis of blood vessels and fibrosis regions in whole slide pathology images.

Download this Poster to Discover:

  • How our AI image analysis platform enables precise and specific analysis of blood vessels and fibrosis in whole slide pathology images and regions of interest (ROI), resulting in improved and unbiased analysis results that outperform traditional pathology analysis methods.

  • The methods used to extract the number of blood vessels and the ratio of fibrosis in whole slide pathology images and regions of interest (ROI) using software algorithms.

  • The benefits of our platform, including real-time display of recognition results, automatic report generation, and the ability to process multiple images simultaneously.

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