Spectral-TCAV for Pairwise Concept-Based Interpretability in Raman Classifiers

Testing with Concept Activation Vectors (TCAV) interprets neural network predictions in terms of human-defined concepts, but has not been applied to Raman spectroscopy, where interpretability is usually limited to individual spectral bands or perturbation responses. We introduce Spectral-TCAV, adapting TCAV to Raman spectra for bacterial species classification, and show that it recovers spectrally meaningful concepts underlying model predictions. We further extend the framework from single spectral regions to pairs of regions, decomposing their interaction into three contrasts: Sum (joint magnitude), Diff (relative dominance), and Tradeoff (antagonistic behavior, isolated by masking for opposite-sign changes). Each contrast is binarized and evaluated with independent linear CAVs and TCAV directional-derivative scores tested against null distributions from randomly sampled regions. Across classes, the same region pair can diverge in significance and direction across contrasts, showing that Sum, Diff, and Tradeoff capture complementary information beyond a single concordant-versus-discordant label.