Unsupervised Causal Prototypical Networks for De-biased Interpretable Dermoscopy Diagnosis

TL;DR

This paper introduces CausalProto, an unsupervised causal prototypical network that enhances dermoscopy diagnosis by removing environmental confounders, improving interpretability and accuracy without sacrificing performance.

Contribution

It proposes a novel unsupervised causal disentanglement framework using structural causal models and do-calculus for bias mitigation in dermoscopy image analysis.

Findings

Outperforms standard models in diagnostic accuracy.

Provides transparent visual interpretability.

Reduces bias from environmental confounders.

Abstract

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.