May
Tuesday, May 26, 2026
8:00am-9:00am PT
RSVP for Webinar
Ruijiang Li, PhD
Associate Professor of Radiation Oncology
Stanford University
Title: Multimodal Foundation Models for Precision Oncology
Abstract: Clinical decision-making demands the integration of diverse data — medical images, clinical narratives, and omics profiles. AI methods that effectively leverage multi-modal data hold transformative potential for patient care. A particularly promising domain is digital pathology, where AI has demonstrated strong capacity to enhance cancer diagnosis and inform treatment strategies. This talk explores how multi-modal foundation models can extract clinically meaningful insights from digital pathology, with a focus on prognostic and predictive biomarkers for precision oncology. I will present our recent work developing vision-language foundation models and approaches that integrate histopathology with spatial proteomics, illustrating how these advances can improve diagnostic accuracy and guide precision cancer treatment.
Dr. Ruijiang Li's lab is focused on the development and application of novel machine learning and deep learning approaches for medical imaging analysis and precision oncology. This can lead to discovery of imaging-based biomarkers for several clinical applications including cancer detection and diagnosis, treatment response and prognosis prediction, which have the potential to transform cancer care.
The Li lab's work spans across multiple imaging domains and modalities including radiology as well as histopathology image data. These data sets are linked with clinical outcomes to address a specific unmet clinical need. Further, they integrate imaging with matched genomic/molecular data to gain more insight into cancer biology.
Artificial intelligence (AI) including machine learning and deep learning plays a critical role in these endeavors. The lab is developing new methods to make these sophisticated models more robust, reproducible, and interpretable; all are which key elements of successful AI applications in medicine.
The Li lab's research is multidisciplinary by nature. They work closely with a team of expert clinicians including oncologists, surgeons, radiologists, and pathologists at Stanford and beyond. Their goal is to translate new technology and imaging biomarkers to clinical practice, which can guide personalized management and therapy selection, ultimately improving outcomes for cancer patients.