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Synergistic Information Disentanglement can significantly boost few-shot learning performance in computational pathology by preserving unique diagnostic signals across modalities.
Non-monotonic performance in experimental trajectories reveals that later rounds can sometimes undermine earlier successes, challenging conventional assumptions in industrial ML research.
