Quantitative pathology and biomarker analysis play a critical role in precision medicine, enabling objective assessment of tissue and cellular features to inform disease diagnosis and treatment. Traditional manual analysis is often time-consuming and subject to variability. AI techniques, including machine learning and deep learning, provide scalable, accurate, and reproducible methods for quantitative analysis of histopathology images. This workshop will cover AI-driven approaches for automated tissue segmentation, feature extraction, and biomarker quantification. Participants will also explore how AI models can correlate image-derived features with clinical and molecular data to discover and validate novel biomarkers. Real-world case studies will demonstrate the impact of AI in improving disease diagnosis and personalized treatment planning.
Aim
This workshop aims to introduce participants to the application of Artificial Intelligence (AI) in quantitative pathology and biomarker analysis. Participants will learn how AI can automate image analysis, quantify tissue and cellular features, and identify disease biomarkers to improve diagnostics, prognostics, and personalized medicine strategies.
What Participants Will Learn
Understand the principles of quantitative pathology and biomarker analysis.
Learn how AI can automate tissue segmentation and feature quantification.
Gain hands-on experience with deep learning models for image analysis.
Explore AI-driven approaches for biomarker discovery and validation.
Apply AI techniques to integrate pathology and clinical data for personalized medicine.
