Description: Four 1.5 hour live sessions held over two weeks
Presenter: Tianyu Liu
Format: Online via Zoom
Time: 12:00pm-1:30pm EST/9:00am-10:30am PST
September 2, 2026
The course opens with an overview of pathology foundation models (Session 1), establishing the conceptual groundwork for how large-scale, pretrained models are reshaping the analysis of digital pathology images.
September 4, 2026
Building on this foundation, Session 2 examines the integration of omics data with pathology images, exploring how foundation models can fuse molecular and morphological information to enable richer, multimodal biological insight.
September 9, 2026
Session 3 surveys recent advances in multimodal foundation models specifically tailored to digital pathology, highlighting state-of-the-art architectures and methods that jointly reason across diverse data types such as histology images, text, and molecular profiles.
September 11, 2026
The course concludes with Session 4, which looks ahead to generative AI for pathology modeling, considering how generative techniques may support tasks like image synthesis, data augmentation, and predictive modeling of tissue and disease states under the augmentation of natural language.
Together, the sessions trace a coherent arc—from understanding what pathology foundation models are, to integrating multiple data modalities, to leveraging generative capabilities. Registrants can gain a structured perspective on how foundation models are advancing precision pathology and computational biomedicine, equipping them to engage critically with this rapidly evolving field.