Kabilan Elangovan

Standard accuracy metrics can mask critical inconsistencies in explanation maps, but the new C-Score reveals impending model instability *before* accuracy drops, offering an early warning for safer clinical deployment.

Fine-tuning can fundamentally rewire what a model "sees" in medical images, even when diagnostic accuracy holds steady, and this rewiring is heavily influenced by the model architecture itself.