Generalized Biomedicine Discovery

In real-world clinical practice, medical images face open-world shifts: (i) long-tailed rare diseases, (ii) subtle lesions dominated bynormal anatomy, and (iii) hierarchical taxonomies. Yet most open-worldparadigms assume flat, balanced label spaces, leaving these biomedi-cal demands unresolved. We introduce Generalized Biomedicine Dis-covery (GBD) and a unified benchmark spanning long-tail, anomaly,and taxonomy-aware discovery. Our key insight is that dominant knownpatterns form a visual manifold that masks subtle novelty. Inspired byexpert diagnosis, we propose SCAN (Surprise-evoked ComplementaryAccommodatioN), which follows a cognition-inspired perceptual progres-sion: it applies predictive suppression to filter expected norms, trig-gers surprise-evoked salience to highlight unexpected deviations, andperforms complementary accommodation to integrate these shifts intoglobal representations. Extensive experiments show that SCAN improvesnovel concept discovery while generally preserving established clinicalknowledge, and it plugs into existing architectures to better navigatethe known–unknown trade-off in medical imaging. Code is available atgithub.com/lytang63/generalized-biomedicine-discovery.