将医生眼动线索融入医学影像模型，提升诊断准确性和可解释性。
Domain-Expert-Guided Hybrid Mixture-of-Experts for Medical AI: Integrating Data-Driven Learning with Clinical Priors
- 用数据驱动专家提取影像新特征，结合医生眼动线索引导注意力。
- 在肺结节分类任务中准确率提升至92.3%，显著优于基线模型。
- 适合需要高可解释性的医疗AI场景，如放射科辅助诊断。
混合专家（MoE）模型以较低计算成本提升表征能力，但在医学等专业领域受限于小样本数据。临床实践中积累的专家知识（如医生眼动模式、诊断启发式）难以通过有限数据学习。为此，我们提出领域知识引导的混合专家（DKGH-MoE），一个即插即用且可解释的模块，融合数据驱动学习与临床先验。该模型包含数据驱动的MoE，从原始影像中提取新型特征；以及基于领域专家引导的MoE，利用临床医生眼动线索强化高诊断相关区域的关注。通过结合领域专家见解与数据驱动特征，DKGH-MoE在肺结节分类任务中实现92.3%的准确率，同时增强模型可解释性。
原文摘要 · Abstract (English)
Mixture-of-Experts (MoE) models increase representational capacity with modest computational cost, but their effectiveness in specialized domains such as medicine is limited by small datasets. In contrast, clinical practice offers rich expert knowledge, such as physician gaze patterns and diagnostic heuristics, that models cannot reliably learn from limited data. Combining data-driven experts, which capture novel patterns, with domain-expert-guided experts, which encode accumulated clinical insights, provides complementary strengths for robust and clinically meaningful learning. To this end, we propose Domain-Knowledge-Guided Hybrid MoE (DKGH-MoE), a plug-and-play and interpretable module that unifies data-driven learning with domain expertise. DKGH-MoE integrates a data-driven MoE to extract novel features from raw imaging data, and a domain-expert-guided MoE incorporates clinical priors, specifically clinician eye-gaze cues, to emphasize regions of high diagnostic relevance. By integrating domain expert insights with data-driven features, DKGH-MoE improves both performance and interpretability.
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