Overview
This workshop will explore how foundation models—a powerful class of advanced AI models —can transform cancer research and clinical care. We will focus on their potential to improve diagnosis, prognosis, and treatment response, with a strong emphasis on clinical translation and technology development.
Key Topics:
- Foundation Model Primer: A high-level introduction to foundation models.
- Multimodal Data: Combining pathology, radiology, omics, and patient data into unified models.
- Prediction: Predicting therapeutic response, resistance, and patient outcomes.
- Validation and Reproducibility: Ensuring model results are consistent and reliable for real-world clinical performance and use.
- Diagnostic Case Studies: Real-world applications for early detection and automated diagnostics.
- Federated Learning: Approaches to training robust models across multiple institutions—without sharing sensitive patient data
- Challenges, Risk, and Regulation: Addressing model interpretability and regulatory considerations for clinical adoption.
Workshop Committee (in alphabetical order):
- Michael Espey, Ph.D., M.T.
- Sean Hanlon, Ph.D.
- Subhashini Jagu, Ph.D.
- Hala Makhlouf, M.D., Ph.D.
- Miguel Ossandon, Ph.D.
- Asif Rizwan, Ph.D.
- Mugdha Samant, Ph.D.
- Shannon Silkensen, Ph.D.
- Brian Sorg, Ph.D., M.B.A.
- Umit Topaloglu, Ph.D.
- Dana Wolff-Hughes, Ph.D.
*For meeting questions, contact:
Asif Rizwan, PhD
Program Director, Diagnostic Biomarkers and Technology Branch
Cancer Diagnosis Program, Division of Cancer Treatment and Diagnosis
National Cancer Institute
National Institutes of Health
9609 Medical Center Dr., 3W530
Rockville, MD 20892
Email: asif.rizwan@nih.gov