Deep learning-enabled filter-free fluorescence microscope.

Optical filtering is an indispensable part of fluorescence microscopy for selectively highlighting molecules labeled with a specific fluorophore and suppressing background noise. However, the utilization of optical filtering sets increases the complexity, size, and cost of microscopic systems, making them less suitable for multifluorescence channel, high-speed imaging. Here, we present filter-free fluorescence microscopic imaging enabled with deep learning-based digital spectral filtering. This approach allows for automatic fluorescence channel selection after image acquisition and accurate prediction of fluorescence by computing color changes due to spectral shifts with the presence of excitation scattering. Fluorescence prediction for cells and tissues labeled with various fluorophores was demonstrated under different magnification powers. The technique offers accurate identification of labeling with robust sensitivity and specificity, achieving consistent results with the reference standard. Beyond fluorescence microscopy, the deep learning-enabled spectral filtering strategy has the potential to drive the development of other biomedical applications, including cytometry and endoscopy.