Histopathology, the current gold standard for cancer diagnosis, involves the manual examination of tissue samples after chemical staining, a time-consuming process requiring expert analysis. Raman spectroscopy is an alternative, stain-free method of extracting information from samples. Using nnU-Net, we trained a segmentation model on a novel dataset of spatial Raman spectra aligned with tumour annotations, achieving a mean foreground Dice score of 80.9%, surpassing previous work. Furthermore, we propose a novel, interpretable, prototype-based architecture called RamanSeg. RamanSeg classifies pixels based on discovered regions of the training set, generating a segmentation mask. Two variants of RamanSeg allow a trade-off between interpretability and performance: one with prototype projection and another projection-free version. The projection-free RamanSeg outperformed a U-Net baseline with a mean foreground Dice score of 67.3%, offering a meaningful improvement over a black-box training approach.
@article{arxiv.2602.18119,
title = {RamanSeg: Interpretability-driven Deep Learning on Raman Spectra for Cancer Diagnosis},
author = {Chris Tomy and Mo Vali and David Pertzborn and Tammam Alamatouri and Anna Mühlig and Orlando Guntinas-Lichius and Anna Xylander and Eric Michele Fantuzzi and Matteo Negro and Francesco Crisafi and Pietro Lio and Tiago Azevedo},
journal= {arXiv preprint arXiv:2602.18119},
year = {2026}
}