🤖 AI Summary
This study addresses the vulnerability of deep learning-based dermatological diagnostic models to shortcut learning, wherein reliance on background artifacts such as skin color compromises fairness and robustness. To mitigate this issue, we propose CAMEO, a novel framework that introduces an explainable AI (XAI)-guided data augmentation mechanism. By leveraging class activation mapping, CAMEO achieves annotation-free lesion localization and background decoupling, subsequently eliminating spurious correlations through synthetic skin replacement. Evaluated on the HAM10000 dataset, the proposed method reduces background-driven errors by nearly fourfold while preserving classification accuracy. Furthermore, it significantly enhances cross-skin-tone generalization, effectively reconciling diagnostic precision with algorithmic fairness in automated dermatological assessment.
📝 Abstract
Deep learning classifiers for dermoscopic skin lesions often reach high in-distribution accuracy while quietly relying on spurious background cues such as skin tone, device vignetting, and embedded rulers, rather than on lesion morphology. This undermines robustness and fairness across skin tones. This work asks whether Explainable AI (XAI), typically used only to audit a finished model, can instead be repurposed as an active training signal that corrects this shortcut without sacrificing diagnostic accuracy. We introduce CAMEO (Class Activation Mapped Equitable Overlay), a framework that improves skin-lesion classification by selecting stable model explanations and using them to separate lesions from their backgrounds. It then replaces the background with realistic synthetic skin while keeping the lesion unchanged. On HAM10000 and dark-skin ISIC images, CAMEO maintained accuracy while reducing background-driven errors by nearly four times. It also made the model's attention more consistent when backgrounds changed. Results across multiple tests show that reducing reliance on background information improves robustness, with Fitzpatrick-based backgrounds providing a realistic and interpretable approach. Results show that XAI-guided augmentation can make dermoscopic classifiers measurably more robust and fair at no cost to accuracy. They also clarify that it is the mechanism and not the specific tone palette that matters, and that the lasting contribution of XAI here lies in stability-screened, annotation-free lesion localisation rather than in the robustness number itself.