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.

翻译：组织病理学作为当前癌症诊断的金标准，涉及对化学染色后组织样本的人工检查，这一过程耗时且需要专家分析。拉曼光谱是一种无需染色的替代方法，可从样本中提取信息。我们利用nnU-Net在新型空间拉曼光谱数据集（与肿瘤标注对齐）上训练了分割模型，获得了80.9%的平均前景Dice分数，超越了先前工作。此外，我们提出了一种新型可解释的基于原型的架构——RamanSeg。该模型通过发现训练集中的特征区域对像素进行分类，并生成分割掩码。RamanSeg的两种变体可在可解释性与性能之间进行权衡：一种采用原型投影机制，另一种为无投影版本。无投影版RamanSeg以67.3%的平均前景Dice分数超越了U-Net基线，相较于黑盒训练方法实现了显著改进。