The increasing availability and resolution of spatially resolved sequencing on human tissue samples, such as Spatial Transcriptomics (ST), provides rich and spatially resolved molecular information to diagnose and analyse tumours beyond the morphological information routinely available to pathologists through Whole Slide Images. Complex morphological and molecular spatial information becoming available at scale requires building robust multimodal AI architectures that take advantage of such high-dimensional information.
This talk will cover recent advancements from our group in building such models, looking at the intersection of robust multimodal learning, learning from hierarchical structures, and representation learning for spatially resolved transcriptomics.
"You can also join us on Zoom":https://cam-ac-uk.zoom.us/j/83400335522?pwd=LkjYvMOvVpMbabOV1MVTm8QU6DrGN7.1
