Transforming Hyperspectral Data into Insight: The DREAM Approach for Pathology

Abstract

Quantitative pathology remains limited due to the need for chemical staining and subjective interpretation of tissue features. Autofluorescence imaging offers a label-free alternative: however, high-dimensional excitation-emission datasets pose challenges for visualization and reproducible analysis. Here, we present Dimensionality Reduction for Enhanced Autofluorescence Microscopy (DREAM), a method that condenses multi-excitation emission spectra into a compact, information-rich format using phasor-based tools. Applied to unstained esophageal tissue samples, DREAM enables high-contrast visualizations that distinguish key histological structures without the need for exogenous labeling. Quantitative assessments across multiple datasets show DREAM improves colorfulness, sharpness, and consistency over single-laser acquisitions, supporting its potential to advance objective, label-free diagnostics through enhanced spectral visualization.