Faithfulness of saliency explanations in a deep learning classifier for panoramic dental radiographs

This is the authors' abstract. We don't add key points for this paper.

Introduction: Saliency maps are widely used to explain dental imaging classifiers, but their faithfulness has not been measured with perturbation tests. Methods: Four saliency methods, integrated gradients, Grad-CAM, raw gradients and a random baseline, were generated for a transfer-learning classifier of four dental diagnoses in the DENTEX dataset and evaluated with deletion and insertion tests on 37 correctly classified teeth. Results: In the insertion test Grad-CAM achieved the highest area under the curve (0.584), followed by integrated gradients (0.482) and raw gradients (0.466), all above the random baseline (0.359). The deletion test produced a different ordering, in which the random baseline showed the lowest area (0.368). Faithfulness varied across diagnosis classes. Conclusions: Gradient-based saliency methods highlight decision-relevant regions more faithfully than a random baseline by the insertion criterion, with Grad-CAM the most faithful, but the disagreement between deletion and insertion tests warrants caution in interpreting any single saliency map.