Machine Learning Working Group virtual meeting, July 19th, 12pm-1pm ET

Please join us for a virtual Machine Learning Working Group Meeting on July 19th, from 12pm-1pm EST. RuiJiang Li, PhD, Assistant Professor in the Department of Radiation Oncology at Stanford University School of Medicine, will be presenting: Artificial intelligence and imaging for personalized cancer treatment.

 

Abstract: 

Radiomics, the high-throughput extraction of features from images, has been widely used to

search for prognostic and predictive imaging signatures in many cancer types. While promising

results were reported, there has been little progress in clinical translation, likely due to a lack of

reproducibility. In addition to radiomics that relies on domain expertise for designing features,

deep learning (DL) has emerged as a powerful approach to discover novel imaging biomarkers

in cancer. While most studies are focused on disease detection and diagnosis using static

image, there is much less investigation on using deep learning for prediction of treatment

response and outcomes, which are different and more challenging clinical problems. In this

presentation, I will discuss some recent progress in the field: 1), making radiomics robust and

broadly applicable; 2), using deep learning to extract dynamic information from longitudinal

imaging; 3), biology-guided radiomics and deep learning.

 

 

The Center for Computational Imaging and Personalized Diagnostics has partnered with the Case Comprehensive Cancer Center to host the Machine Learning Working Group. Meetings are held with the express goal of leveraging the extensive Artificial Intelligence expertise concentrated across our partner institutions including Case Western Reserve University, University Hospitals, the Cleveland VA Medical Center, MetroHealth, and the Cleveland Clinic and beyond. Through facilitating the interaction of basic and clinical researchers we hope to strengthen the scientific merit of presented studies as well as identify novel collaborative opportunities. 

 

If you would like to attend this month’s session, please reach out to James Hale - jsh171@case.edu