Unsupervised Causal Prototypical Networks for De-biased Interpretable Dermoscopy Diagnosis
This addresses trust issues in medical AI by reducing misleading visual evidence in dermoscopy diagnosis, though it appears incremental as an enhancement to existing prototypical networks.
The paper tackles the problem of selection bias in clinical dermoscopy data causing deep learning models to learn spurious correlations, proposing CausalProto to purify visual evidence through causal disentanglement. The method achieves superior diagnostic performance on multiple datasets while providing transparent interpretability without accuracy compromise.
Despite the success of deep learning in dermoscopy image analysis, its inherent black-box nature hinders clinical trust, motivating the use of prototypical networks for case-based visual transparency. However, inevitable selection bias in clinical data often drives these models toward shortcut learning, where environmental confounders are erroneously encoded as predictive prototypes, generating spurious visual evidence that misleads medical decision-making. To mitigate these confounding effects, we propose CausalProto, an Unsupervised Causal Prototypical Network that fundamentally purifies the visual evidence chain. Framed within a Structural Causal Model, we employ an Information Bottleneck-constrained encoder to enforce strict unsupervised orthogonal disentanglement between pathological features and environmental confounders. By mapping these decoupled representations into independent prototypical spaces, we leverage the learned spurious dictionary to perform backdoor adjustment via do-calculus, transforming complex causal interventions into efficient expectation pooling to marginalize environmental noise. Extensive experiments on multiple dermoscopy datasets demonstrate that CausalProto achieves superior diagnostic performance and consistently outperforms standard black box models, while simultaneously providing transparent and high purity visual interpretability without suffering from the traditional accuracy compromise.