GraphSkinUQ: A Hybrid Transfer Learning Approach for Feature Extraction and Graph Transformer-Based Uncertainty Quantification for Robust Skin Cancer Classification

Abstract

The early and reliable diagnosis of skin cancer is critical to mitigating its metastatic potential and progression. However, even highly accurate artificial intelligence (AI) classifiers tend to produce overconfident predictions on ambiguous lesions, increasing the risk of misdiagnosis. Uncertainty quantification (UQ) addresses this challenge by evaluating model confidence and distinguishing definitive classifications from cases requiring clinician review—a cornerstone for safely integrating AI into clinical practice. We propose GraphSkinUQ, a hybrid framework that integrates convolutional neural network (CNN)-based feature extraction and graph-based relational reasoning to model lesion contextual relationships, together with Bayesian Monte Carlo dropout, to simultaneously quantify epistemic uncertainty (model limitations) and aleatoric uncertainty (data noise). Through iterative sampling of the predictive posterior distribution, GraphSkinUQ generates well-calibrated confidence scores, evaluated using average predictive entropy (0.10 bits), Brier score (0.1382), and expected calibration error (ECE). Experimental results show that GraphSkinUQ achieves a Brier score of 0.1382, Receiver Operating Characteristic (ROC) area under the curve (AUC) of 0.954, and average predictive entropy of 0.10, outperforming conventional classifiers in calibration. The framework effectively identifies high-uncertainty cases, allowing clinicians to prioritize ambiguous lesions for additional scrutiny. By bridging AI-driven diagnostics with clinically interpretable confidence metrics, GraphSkinUQ enhances the safety and transparency of automated skin cancer screening, fostering trustworthy and robust human–AI collaboration in clinical decision-making, and aligning machine-driven insights with the nuanced demands of real-world oncology practice.