A Masked Image Modeling Approach to CyCIF Panel Reduction and Marker Imputation

Cyclic Immunofluorescence (CyCIF) has emerged as a powerful technique that can measure multiple biomarkers in a single tissue sample but it is limited in panel size due to technical issues and tissue loss. We develop a computational model that imputes a surrogate in silico high-plex CyCIF from only a few experimentally measured biomarkers by learning co-expression and morphological patterns at the single-cell level. The reduced panel is optimally designed to enable full reconstruction of an expanded marker panel that retains the information from the original panel necessary for downstream analysis. Using a masked image modeling approach based on the self-supervised training objective of reconstructing full images at the single-cell level, we demonstrate significant performance improvement over previous attempts on the breast cancer tissue microarray dataset. Our approach offers users access to a more extensive set of biomarkers beyond what has been experimentally measured. It also allows for allocating resources toward exploring novel biomarkers and facilitates greater cell type differentiation and disease characterization. Additionally, it can handle assay failures such as low-quality markers, technical noise, and/or tissue loss in later rounds as well as artificially upsample to include additional panel markers.