Our understanding of tissue biology has been transformed in recent years by highly multiplexed imaging that can capture the spatial organization of proteins in tissues. This has also made it possible to collect tissue images at a resolution that was previously unattainable. This kind of data can be produced using a number of technologies, including CyCIF, MIBI, CODEX, and IMC. However, tissue folds, detritus, and antibody aggregates can occur as a result of improper sample handling during sample preparation. These artifacts can affect downstream analysis and make it more complicated. I use the vision foundational model DINOv3 to learn representations from highly multiplexed images. I examine whether this model can effectively learn to identify and detect artefacts in highly multiplexed images providing automated quality control.

Co-authors: Matthias Meyer-Bender, Wolfgang Huber