Healthcare & Medical AIComputer Vision & Perception

Vision Transformer-Based Autism Detection

Ongoing

A Vision Transformer framework with multimodal fusion of facial and behavioral signals for early, interpretable Autism Spectrum Disorder screening in toddlers.

Vision Transformer-based autism spectrum disorder screening from facial and behavioral signals

The idea

Autism Spectrum Disorder screening today mostly depends on clinician-administered behavioral assessments, which are slow to access and unevenly available. This project builds a Vision Transformer (ViT) framework that fuses facial imaging with behavioral signal data into a single multimodal model, aiming at scalable, low-cost early screening for ASD in toddlers that could run outside a clinical setting.

Status

The goal is an interpretable, low-friction screening tool rather than a diagnostic replacement — explainability matters here because a false negative or an opaque prediction in a pediatric health context has real consequences. The work is ongoing and has been submitted for journal publication; method and result details are held back until the review process concludes.

Tech stack & key skills

Core tools, methods and skills demonstrated in this project:

Vision Transformer (ViT)Multimodal fusionFacial imaging analysisBehavioral signal modelingExplainable / interpretable AIEarly screening / healthcare AI