Raman spectral unmixing of breast cancer tissues via continuous wavelet transform and TransUnet.

Raman spectroscopy has been proved to have the potential to accurately diagnose a variety of diseases, and novel Raman probes or instruments for clinical applications have been constantly developed. However, biological tissues are usually structurally complex. The Raman signals collected in vivo may be a mixture of various chemical components, even different tissues, which poses challenges for disease analysis and diagnosis. This work proposed a Raman spectral unmixing approach to separate the signals of different tissues from their mixed spectra. Specifically, continuous wavelet transform was performed to extract the multi-scale time-frequency domain features of Raman spectra. TransUnet model was introduced to analyze the multi-scale features from high-frequency to low-frequency through the convolution and transformer modules, and predict the Raman signals of target components. Breast cancer tissues were selected as the research subject, the Raman signals of stroma and adipocyte were successfully separated from their mixed tissues, and multiple biochemical changes in breast cancer tissues were revealed through further analysis of the unmixing signals. This work will contribute to biological in vivo detection of Raman probes or instruments, enabling them to separate signals from different tissues, structures, and even biochemical molecular components for more detailed and accurate analysis of diseases.