MedAI

Title: AI-driven fast and accurate cell phenotyping in highly multiplex images

Speaker: Muhammad Shaban

Abstract:
Highly multiplexed protein imaging is emerging as a potent technique for analyzing protein distribution within cells and tissues in their native context. However, existing cell annotation methods utilizing high-plex spatial proteomics data are resource intensive and necessitate iterative expert input, thereby constraining their scalability and practicality for extensive datasets. We introduce MAPS (Machine learning for Analysis of Proteomics in Spatial biology), a machine learning approach facilitating rapid and precise cell type identification with human-level accuracy from spatial proteomics data. Validated on multiple in-house and publicly available MIBI and CODEX datasets, MAPS outperforms current annotation techniques in terms of speed and accuracy, achieving pathologist-level precision even for typically challenging cell types, including tumor cells of immune origin. By democratizing rapidly deployable and scalable machine learning annotation, MAPS holds significant potential to expedite advances in tissue biology and disease comprehension.

Speaker Bio:
Dr. Shaban is a Machine Learning Scientist at the AI for Pathology Image Analysis Lab, associated with Mass General Brigham and Harvard Medical School. This role follows his completion of over two years of post-doctoral research at the same lab. He earned his PhD in Computer Science from the University of Warwick, focusing on medical image analysis. He has over eight years of research experience in computer vision, deep learning, and AI-driven medical image analysis. His primary focus is developing advanced algorithms for critical clinical applications in computational pathology. For further information, please visit his website at www.mshaban.org.

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