审计Sybil：通过生成干预性归因解释深度肺癌风险预测
Auditing Sybil: Explaining Deep Lung Cancer Risk Prediction Through Generative Interventional Attributions
AI总结：
本文提出S(H)NAP框架，通过专家放射科医生验证的生成干预性归因，揭示Sybil模型在区分恶性肺结节与良性病变时的失效模式，包括对不合理伪影的敏感性和径向偏差。
摘要：
肺癌仍是癌症死亡的主要原因，推动了自动化筛查工具的发展。Sybil是一种深度学习模型，能从CT图像高精度预测未来风险。然而，尽管经过广泛临床验证，当前评估仅依赖观测指标。基于相关性的方法忽略了模型的实际推理机制，需转向因果验证以确保临床部署前的决策鲁棒性。本文提出S(H)NAP框架，通过专家放射科医生验证的生成干预性归因，系统修改解剖特征以隔离对象特定的因果贡献。首次对Sybil进行干预性审计，表明模型虽常表现出类似专家放射科医生的行为，但在区分恶性肺结节与良性病变时存在关键失效模式，包括对临床不合理伪影的敏感性和明显的径向偏差。
英文摘要：
Lung cancer remains the leading cause of cancer mortality, driving the development of automated screening tools to alleviate radiologist workload. Standing at the frontier of this effort is Sybil, a deep learning model capable of predicting future risk solely from computed tomography (CT) with high precision. However, despite extensive clinical validation, current assessments rely purely on observational metrics. This correlation-based approach overlooks the model's actual reasoning mechanism, necessitating a shift to causal verification to ensure robust decision-making before clinical deployment. We propose S(H)NAP, a model-agnostic auditing framework that constructs generative interventional attributions validated by expert radiologists. By leveraging realistic 3D diffusion bridge modeling to systematically modify anatomical features, our approach isolates object-specific causal contributions to the risk score. Providing the first interventional audit of Sybil, we demonstrate that while the model often exhibits behavior akin to an expert radiologist, differentiating malignant pulmonary nodules from benign ones, it suffers from critical failure modes, including dangerous sensitivity to clinically unjustified artifacts and a distinct radial bias.
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