Interpreter of Maladies: Application of Machine Learning to Precision Oncology
In this webinar, Emory University’s Dr. Anant Madabhushi will share how radiomic and pathomic approaches (derived from machine learning tools) can help predict disease outcome, recurrence, progression, and therapy response in the context of cancer.
Such approaches can assist your work in:
- prostate cancer.
- brain cancer.
- rectal cancer.
- oropharyngeal cancer.
- lung cancer.
Dr. Madabhushi and his team have been developing computerized knowledge alignment, representation, and fusion tools for integrating and correlating heterogeneous biological data. Such machine learning tools are valuable for precision oncology.
Dr. Madabhushi is a professor at the Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University. He also serves as executive director at the Emory Empathetic Artificial Intelligence (AI) for Health Institute. His team applies AI, radiomics, computational pathology, medical image analysis, and computer vision to the diagnosis, prognosis, and prediction of treatment responses across diseases (including cancer).
Upcoming Events
- Non-invasive Biomarkers for irAE and IMTX Response: Computational Science in Immuno-oncologyJune 18, 2025Data Jamboree: Enhancing Childhood Cancer Data Sharing and UtilitySeptember 29, 2025 - September 30, 2025NCI Office of Data Sharing’s Annual Data Sharing Symposium 2025: How Data Advances the Impact of Cancer ResearchSeptember 30, 2025 - October 01, 2025Childhood Cancer Data Initiative (CCDI) Symposium 2025October 06, 2025 - October 07, 2025