无监督因果原型网络用于去偏的可解释皮肤病镜诊断
Unsupervised Causal Prototypical Networks for De-biased Interpretable Dermoscopy Diagnosis
AI总结：
CausalProto通过无监督因果原型网络去偏可解释皮肤病镜诊断，提升诊断性能并提供高纯度视觉解释。
摘要：
尽管深度学习在皮肤病镜图像分析中取得了成功，但其固有的黑箱性质阻碍了临床信任，促使使用原型网络进行基于案例的视觉透明性。然而，临床数据中不可避免的选取偏见往往会使这些模型倾向于捷径学习，其中环境混杂物被错误地编码为预测原型，生成虚假的视觉证据，误导医疗决策。为了缓解这些混淆效应，我们提出了CausalProto，一种无监督因果原型网络，从根本上净化了视觉证据链。在结构因果模型框架下，我们采用信息瓶颈约束的编码器，强制严格无监督的正交解耦之间病理特征和环境混杂物。通过将这些解耦的表示映射到独立的原型空间，我们利用学习到的虚假字典通过do-演算进行后门调整，将复杂的因果干预转化为高效的期望池化，以消除环境噪声。在多个皮肤病镜数据集上的广泛实验表明，CausalProto在诊断性能上优于标准黑箱模型，并且同时提供了透明且高纯度的视觉可解释性，而不会受到传统准确率妥协的影响。
英文摘要：
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.
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