Whole-slide images often contain heterogeneous tumor patterns, but many MIL methods still assume a single dominant label and provide limited interpretability. PAMIL introduces prototype attention-based multiple instance learning to model multiple histotypes within one slide while producing more meaningful explanations of the reasoning process. This makes whole-slide classification more clinically useful in settings where tumor heterogeneity matters.