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

2026-02-20

AI Summary

This study addresses the limitations of conventional cancer diagnosis, which relies on time-consuming, expert-dependent histopathological staining and lacks rapid, label-free alternatives. To this end, the authors propose RamanSeg—an interpretable deep learning model based on prototype learning for pixel-level tumor segmentation in spatially resolved Raman spectroscopy. The method leverages a stain-free Raman dataset aligned with tumor annotations and introduces two prototype-driven architectures, with and without projection layers, to balance interpretability and performance. Experimental results show that while the nnU-Net baseline achieves an average foreground Dice score of 80.9% on the test set, the non-projection variant of RamanSeg attains 67.3%, significantly outperforming black-box U-Net models. These findings underscore the potential of the proposed approach for label-free cancer diagnosis.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Multi-instance/Multi-view Learning
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Security and Privacy: Data transparency and provenance
• Responsible Web: Algorithmic accountability and transparency on the web

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.

Problem

• Raman spectroscopy
• cancer diagnosis
• interpretability
• histopathology
• stain-free

Innovation

• Raman spectroscopy
• interpretable deep learning
• prototype-based architecture
• cancer diagnosis
• image segmentation