Patient identity hides in Raman spectra: adversarial training cuts ID prediction from 68% to 12% while IDH typing holds at 0.8
In glioma Raman spectra, a single spectrum is enough for a model to tell which patient it came from, a sample-specific bias unrelated to biology that can confound tumor typing. On the 46-sample APOLLO dataset, a learnable feature-importance layer is trained adversarially against a two-headed classifier to suppress patient-identity signal while keeping IDH mutation information. Patient-ID balanced accuracy falls from 68% to 12%, while mutant-versus-wild-type classification stays at 0.8 balanced accuracy.