Pathology AI Foundation Models Across Modalities and Spatial Scales
Attend this data science seminar to explore how AI foundation models are expanding the possibilities of computational pathology. Learn how researchers are developing models that work across different data modalities and spatial scales to uncover biologically meaningful information from tissue and advance precision medicine.
Dr. Andrew Song from UT MD Anderson Cancer Center will:
- highlight the growing role of foundation models in computational pathology and how they can support scalable representation learning and flexible analytical workflows.
- explore how AI models can extend beyond histopathology to incorporate modalities such as spatial transcriptomics and proteomics.
- discuss how multimodal deep learning approaches can help researchers connect information across tissue images and molecular data to gain new insights into disease biology.
By developing pathology AI models that capture information across modalities and spatial scales, researchers can build a more comprehensive view of tissue biology and create new opportunities to study cancer and other diseases.
About the Speaker
Andrew Song, Ph.D.
Dr. Song is an assistant professor in the Department of Translational Molecular Pathology at UT MD Anderson Cancer Center and an adjunct professor in the Department of Computer Science at Rice University. His research focuses on multimodal deep learning in pathology, biomedical signal processing, and statistical inference frameworks.
About the Data Science Seminar Series
CBIIT’s Data Science Seminar Series is dedicating its 2026 events to spotlighting the use of AI in cancer research and care. Brought to you by CBIIT and NCI’s Division of Cancer Treatment and Diagnosis AI working group, the upcoming webinars will explore a variety of questions, such as the following:
- How can AI be used for diagnosis, treatment, or omics research?
- What are the related laws and ethical considerations for AI?
- How can we empower an AI-ready cancer research community through workforce development, collaborations, and funding?
To view upcoming speakers or recordings of past presentations, visit the Data Science Seminar Series page.