50: How to approach colon cancer with supervised deep learning image analysis w/ Rish Pai, Mayo Clinic

Digital Pathology Podcast

Send us a text

This episode is brought to you by Aiforia. Thank you Aiforia :)
Today you will learn how Raish Pai, MD, a busy, practicing pathologist from Mayo Clinic developed a complex supervised deep learning tissue image analysis model to quantify visual diagnostic features of colon cancer and in the process developed a model that can predict clinical outcome.
He used the deep learning-based tissue image analysis platform - Aiforia.
The quantified features included:

• Stromal immune cell Infiltrates
• Immature stroma
• Tumor-Infiltrating Lymphocytes
• Mucin
• Different growth patterns
• & many others

THIS EPISODE'S RESOURCES:

• Podcast with Thomas Westerling-Bui, Aiforia: "What is Validation and how to validate and AI image analysis solution"
• Rish Pai's publication "Quantitative Pathologic Analysis of Digitized Images of Colorectal Carcinoma Improves Prediction of Recurrence-Free Survival"
• Colon Cancer Family Registry website
• Podcast about the BIG PICTURE initiative "BigPicture - the largest whole slide repository for AI model development in pathology. Where do we stand at month 15/ 72?"

THIS EPISODE'S SPECIAL OFFER "THE BETA COHORT"
Join and be part of the co-creation of the only online course like this in the digital pathology world "PATHOLOGY 101 FOR TISSUE IMAGE ANALYSIS".
Learn more about the AMAZING OFFER that awaits you when you join the BETA COHORT today!
!!! Limited time offer!!! The discount expires on November 27th 2022

Learn more HERE
Support the show

Become a Digital Pathology Trailblazer get the "Digital Pathology 101" FREE E-book and join us!