Computational Pathology and Integrative Genomics for Cancer Research

Department of Quantitative Health Sciences
Bioinformatics Core Sponsored
Computational Pathology and Integrative Genomics for Cancer Research

Computational pathology is an emerging research area straddling artificial intelligence, computer vision, and biomedical imaging. It has been boosted in recent years by the wide adoption of deep learning technologies. Besides its applications in disease diagnosis and prognosis, computational pathology is a powerful phenotyping tool for quantitatively measuring cellular and tissue morphology in disease tissues such as cancer biopsy samples. The extracted quantitative morphological features can be integrated with omics data to generate new biological hypothesis regarding cancer development and new integrative markers predicting clinical outcomes for patients. In this talk, Dr. Huang will give any overview of his work in computational pathology over the past fifteen years and recently developed machine learning methods for integrating histopathology images with omics data in cancer research.

Kun Huang, Ph.D., M.S.
Professor and Vice Chair in Biostatistics and Health Data Science
Assistant Dean for Data Science at Indiana University School of Medicine (IUSM)
Associate Director for Data Science of the IU Simon Comprehensive Cancer Center
IUSM PHI Endowed Chair for Genomic Data Science

Thursday, April 15, 2021
12:00 – 1:00 PM HST

For more information and to obtain the Zoom link, contact Ms. Heather Borgard at or Dr. Eunjung Lim at

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