RamanSeg: Interpretability-driven Deep Learning on Raman Spectra for Cancer Diagnosis

• URL: http://arxiv.org/abs/2602.18119v1
• Date: Fri, 20 Feb 2026 10:18:27 GMT
• Title: RamanSeg: Interpretability-driven Deep Learning on Raman Spectra for Cancer Diagnosis
• Authors: Chris Tomy, Mo Vali, David Pertzborn, Tammam Alamatouri, Anna Mühlig, Orlando Guntinas-Lichius, Anna Xylander, Eric Michele Fantuzzi, Matteo Negro, Francesco Crisafi, Pietro Lio, Tiago Azevedo,
• Abstract summary: Histopathology, the current gold standard for cancer diagnosis, involves the manual examination of tissue samples after chemical staining.<n>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%.<n>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.
• Score: 7.438726554579399
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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