Ultrafast Labeling for Multiplexed Immunobiomarkers from Label-free Fluorescent Images

Abstract

Labeling pathological images based on different immunobiomarker holds immense clinical significance, serving as an instrumental tool in various fields such as disease diagnostics and biomedical research. However, the existing predominant techniques harnessed for immunobiomarker labeling, such as immunofluorescence (IF) and immunohistochemistry (IHC), are marred by shortcomings such as inconsistent specificity, cost/time-intensive staining procedures, and potential cellular damage incurred during labeling. In response to these impediments, deep-learning-powered generative models have emerged as a promising avenue for immunolabeling prediction, owing to their adeptness in image-to-image translation. To realize automatic immunolabeling prediction, we devised an auto-immunolabeling (Auto-iL) network capable of simultaneous labeling various immunobiomarkers by generating the corresponding immunofluorescence-stained images from dual-modal label-free inputs. To enhance the feature extraction potential of the Auto-iL network, we utilize random masked autoencoders on dual-modal. Subsequently, a self-attention block adeptly merges the dual features, which empowers a robust predictive capacity. In the experiments, immunolabeling performance of four biomarkers for gastric cancer patients was validated. Moreover, pathologists carried out clinical observation assessments on the immunolabeled results to ensure the reliability at the cellular level.