用拉曼光谱实现癌症诊断，模型可解释且分割准确率超80%
RamanSeg: Interpretability-driven Deep Learning on Raman Spectra for Cancer Diagnosis
- 基于原型的可解释架构，通过学习训练集特征区域进行像素分类
- 投影无依赖版本达67.3%平均前景Dice，优于黑箱模型
- 适用于需要透明决策过程的医学影像分析场景
组织病理学是当前癌症诊断的金标准，需对染色组织样本进行人工显微检查，耗时且依赖专家。拉曼光谱是一种无需染色的替代方法，可提取样品信息。我们使用nnU-Net在新构建的空间拉曼光谱数据集上训练分割模型，该数据集与肿瘤标注对齐，取得80.9%的平均前景Dice分数，优于先前工作。此外，提出一种新型可解释的原型驱动架构RamanSeg，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有两种变体，分别在可解释性与性能间权衡：一种带原型投影，另一种无投影。无投影版本的RamanSeg以67.3%的平均前景Dice分数超越U-Net基线，显著优于黑箱训练方法。
原文摘要 · Abstract (English)
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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