YoVDO

Leveraging Medical Twitter to Build a Visual Language AI Model for Pathology

Offered By: Computational Genomics Summer Institute CGSI via YouTube

Tags

Machine Learning Courses Deep Learning Courses Pathology Courses Medical Imaging Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore how medical Twitter data can be leveraged to build a visual language AI model for pathology in this conference talk from the Computational Genomics Summer Institute (CGSI) 2023. Delve into the innovative research presented by James Zou, which combines social media content with advanced AI techniques to enhance pathological analysis. Learn about the integration of spatial gene expression data with breast tumor morphology using deep learning methods. Discover the potential of graph deep learning for characterizing tumor microenvironments from spatial protein profiles in tissue specimens. Gain insights into the development of the 7-UP method for generating in silico CODEX data from a limited set of immunofluorescence markers. Explore the identification of spatial cellular structures using the SPACE-GM approach. This talk offers a comprehensive overview of cutting-edge techniques that bridge the gap between social media, artificial intelligence, and pathology, potentially revolutionizing disease diagnosis and treatment strategies.

Syllabus

James Zou | Leveraging Medical Twitter to Build a Visual language AI Model for Pathology | CGSI 2023


Taught by

Computational Genomics Summer Institute CGSI

Related Courses

Image and Video Processing: From Mars to Hollywood with a Stop at the Hospital
Duke University via Coursera
Statistical Shape Modelling: Computing the Human Anatomy
University of Basel via FutureLearn
Les deux infinis et nous - Voyages de l'infiniment grand à l'infiniment petit
École Polytechnique via Coursera
Life, Health and Radiation
The University of Sydney via Coursera
Toward automatic digital pathology image analysis
Universidad Politécnica de Madrid via Miríadax