用面部区域引导眼动建模，提升自闭症筛查准确率
AOI-Net: Structural Face AOI-Guided Eye-Gaze Track Representation Learning for Autism Spectrum Disorder Detection
- 结合眼动短期动态与面部区域结构信息建模
- 在1300+参与者数据上超越现有方法，准确率显著提升
- 适合临床自闭症早期筛查与可解释AI研究
眼动追踪作为自闭症谱系障碍（ASD）无创筛查的有前景方法，已在社交互动任务中观察到注意力分配与回看行为的系统性差异。现有计算方法通常依赖离散注视轨迹和固定点事件刻画眼动，其表征受短时动态主导，难以捕捉长程依赖；而眼动自然依附于语义有意义的注意区域（AOIs），其注意力分配与转移蕴含重要结构线索，但鲜有方法显式建模。为此，我们提出结构化面部AOI引导的眼动轨迹网络（AOI-Net），联合建模短时动态与AOI层级结构组织。通过网络门控机制自适应融合互补的时空表征，根据对眼动行为刻画的贡献进行加权。为缓解临床数据集中ASD与典型发育（TD）个体间显著的类别不平衡问题，进一步引入类分布感知学习，以在偏斜分布下实现更具判别性的嵌入学习。在包含八个刺激子集、超过1300名参与者的独特大规模临床眼动数据库上的实验表明，AOI-Net持续优于现有最先进方法。所提框架支持可解释的眼动行为建模，为真实医疗环境中可扩展的AI驱动自闭症筛查提供了实用基础。代码已开源。
原文摘要 · Abstract (English)
Eye-movement tracking has emerged as a promising non-invasive approach to Autism Spectrum Disorder (ASD) screening, with systematic differences in attentional allocation and revisit behaviors observed during socially interactive tasks. Existing computational methods typically characterize eye-movements using discrete gaze trajectories and fixation events, yielding representations dominated by short-range temporal dynamics and limiting models that primarily emphasize long-range dependencies. Meanwhile, gaze behavior is naturally organized across semantically meaningful Areas of Interest (AOIs), whose attention allocation and transitions provide important structural cues, yet their relationships are rarely modeled explicitly. To address these limitations, we propose a structural face AOI-guided Eye-Gaze Track Network (AOI-Net) that jointly models short-term temporal dynamics and AOI-level structural organization. A network gating mechanism adaptively integrates the complementary temporal and structural representations according to their contributions to gaze-behavior characterization. To mitigate the pronounced class imbalance commonly encountered between individuals with ASD and Typically Developing (TD) participants in clinical datasets, class-distribution-aware learning is further employed to facilitate discriminative embedding learning under skewed class distributions. Experiments on a unique and large-scale clinical eye-tracking database comprising eight stimulus subsets and more than 1,300 participants show that AOI-Net consistently outperforms state-of-the-art methods. The proposed framework also enables interpretable gaze-behavior modeling and provides a practical basis for scalable AI-driven ASD screening in real-world healthcare. The code is available at https://github.com/Zhanpei-ai/CIM-AOI-Net/tree/main/Code
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