Multimodal AI for Digital Pathology and Spatial Transcriptomics

This 3-day workshop introduces participants to the use of AI and Transformer-based models in spatial biology and digital pathology. Participants will learn how whole-slide images, tissue patches, spatial embeddings, gene expression data, and clinical outcomes can be integrated to study cancer tissue architecture, immune cell patterns, and patient risk prediction. The workshop includes practical Google Colab sessions using open-source tools such as PyTorch, HuggingFace Transformers, Squidpy, Scanpy, OpenSlide, Matplotlib, and Seaborn.

To provide practical understanding of how Vision Transformers and multi-modal AI models can be used to analyze tissue images, spatial genomics data, and cancer microenvironment patterns for research and clinical translation.