AOI-Net：面向自闭症谱系障碍检测的结构性面部感兴趣区域引导眼动轨迹表示学习
AOI-Net: Structural Face AOI-Guided Eye-Gaze Track Representation Learning for Autism Spectrum Disorder Detection
浏览论文内容
中文总结 AI 辅助
该研究针对眼动ASD筛查方法的局限，提出AOI-Net联合建模眼动时间动态与面部AOI结构，结合类别分布感知学习，在1300余人的临床数据集上性能优于现有方法，为AI驱动ASD筛查提供可解释方案。
中文摘要 AI 辅助
眼动追踪已成为一种颇具前景的自闭症谱系障碍（ASD）非侵入性筛查方法，在社交互动任务中，ASD患者与典型发展（TD）个体存在注意力分配和 revisit 行为的系统性差异。现有计算方法通常使用离散的注视轨迹和 fixation 事件来表征眼动，生成的表示以短期时间动态为主，限制了模型对长期依赖关系的建模能力。同时，注视行为自然地按语义有意义的感兴趣区域（AOI）组织，其注意力分配和转换提供了重要的结构性线索，但二者的关系很少被显式建模。为解决这些局限，我们提出结构性面部AOI引导眼动轨迹网络（AOI-Net），联合建模短期时间动态和AOI级结构组织。网络门控机制根据时间和结构表示对注视行为表征的贡献，自适应整合这两种互补表示。为缓解临床数据集中ASD患者与TD个体间常见的显著类别不平衡问题，我们采用类别分布感知学习，以在倾斜类别分布下促进判别性嵌入学习。在包含8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开展的实验表明，AOI-Net的性能始终优于现有最先进方法。该框架还支持可解释的注视行为建模，为现实医疗场景中可扩展的AI驱动ASD筛查提供了实用基础。代码可在指定URL获取。
英文摘要
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
发表机构
- School of Computer Science and Technology, Guangdong University of Technology（广东工业大学计算机科学与技术学院）
- Shenzhen Maternity and Child Healthcare Hospital Affiliated to Southern Medical University（南方医科大学附属深圳市妇幼保健院）
- Department of Computer Science, Hong Kong Baptist University（香港浸会大学计算机科学系）
机构由 AI 辅助整理，请以论文原文为准。
