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ATHENA: Hypergraph-Based Learning for Toddler Autism Classification from Eye-Tracking — arXiv:2611.10818

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.10818
License (as archived): CC0-1.0
Archived: 2026-11-10T18:41:13.715Z
SHA-256 of the archived text: 662f2caf4c910c6a7c58d8485be844524389fac56d533ccad6c7f2ad246a9335
Stable URL of this page: https://metraipolis.com/documento/dc_17455b506832

How to cite

ATHENA: Hypergraph-Based Learning for Toddler Autism Classification from Eye-Tracking — arXiv:2611.10818 — arXiv (en), https://arxiv.org/abs/2611.10818 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_17455b506832 (Metraipolis archive dc_17455b506832, sha256 662f2caf4c910c6a7c58d8485be844524389fac56d533ccad6c7f2ad246a9335)

BibTeX

@misc{dc17455b506832,
  title = {ATHENA: Hypergraph-Based Learning for Toddler Autism Classification from Eye-Tracking — arXiv:2611.10818},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.10818},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_17455b506832; sha256 662f2caf4c910c6a7c58d8485be844524389fac56d533ccad6c7f2ad246a9335},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.10818. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

In this work we proposed a architecture designed for the classification of ASD in toddlers using eye-tracking data, addressing the critical need for early and precise detection. The proposed model incorporates FMMix, a feature-level augmentation technique that bolsters the model's learning capacity, and in this work we proposed the ATHENA, which captures intricate feature relationships via an adaptive hypergraph framework. Evaluated on two datasets dataset of gaze coordinates-the model achieves state-of-the-art performance, with AUC scores of 99.80% and 99.86%, respectively. It also exhibits high accuracy of 98.45% and 98.56%, sensitivity 98.01% and 98.10%, and specificity 98.74% and 99.02%, respectively. Demonstrating its superior ability to identify subtle gaze patterns associated with ASD, these results significantly surpass existing methods, highlighting the model's potential for clinical ASD screening. Future efforts will focus on validation across broader populations and integration with multi-modal data to further refine diagnostic accuracy.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.10818 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-10T18:37:37.505Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).