arXiv:2607. 07957v1 Announce Type: new Abstract: Autism spectrum disorder (ASD) affects over 75 million individuals worldwide, yet scalable computational methods for remote behavioral screening remain limited.
arXiv:2608. 07567v1 Announce Type: cross Abstract: Functional near-infrared spectroscopy (fNIRS) is a promising modality for autism spectrum disorder (ASD) classification, yet existing approaches assume temporally aligned evaluation.
The article reviews 55 machine‑learning studies on autism spectrum disorder (ASD) published between 2017 and 2023, focusing on how ML can aid early diagnosis and treatment. It finds that supervised learning dominates current research, while deep learning is gaining traction as data volumes grow. The review highlights the need for models that fuse complex data—such as genetic, clinical, wearable, and biometric sources—to improve diagnostic accuracy and enable continuous, non‑intrusive monitoring.
arXiv:2609.14159v1 Announce Type: cross Abstract: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition whose early diagnosis remains challenging because conventional clinical assessments...
arXiv:2609.16464v1 Announce Type: cross Abstract: The clinical management of autism spectrum disorder (ASD) faces a bottleneck in early screening, mainly because trained specialists are scarce and co...
AOI-Net introduces a structural face AOI-guided Eye‑Gaze Track Network that jointly models short‑term temporal dynamics and AOI‑level structural organization for Autism Spectrum Disorder detection. The network uses a gating mechanism to adaptively combine complementary representations and incorporates class‑distribution‑aware learning to address the imbalance between ASD and typically developing participants. Experiments on a large clinical eye‑tracking database with over 1,300 participants demonstrate that AOI‑Net outperforms state‑of‑the‑art methods and offers interpretable gaze‑behavior modeling for scalable AI‑driven ASD screening.
UniAR is a unified framework that improves autism spectrum disorder (ASD) recognition by using multi-granularity prompt learning and a large multimodal model to generate diagnostic descriptions at word, phrase, and sentence levels. It aligns these semantic representations with visual evidence through a Mixture-of-Experts-based Multi-Scale Alignment Module, enabling robust ASD detection across heterogeneous data types. Experiments on four brain MRI and facial expression benchmarks show that UniAR outperforms state‑of‑the‑art methods, achieving 75.9% accuracy on MRI and 91.6% on facial benchmarks, with gains of 1.5 and 1.2 percentage points respectively.
arXiv:2608. 06122v1 Announce Type: cross Abstract: Inspired by recent evidence that transformer architectures benefit from Self-PreTraining (SPT) on long-context benchmarks, we investigate whether similar gains extend to multimodal, multivariate, and even simple univariate medical time series.
arXiv:2306.14300v2 Announce Type: replace-cross Abstract: Autism spectrum disorder (ASD) is a developmental condition that presents significant challenges in social interac- tion, communication, and...
arXiv:2607. 12774v1 Announce Type: cross Abstract: This article presents our results for the 11th Affective Behavior Analysis in-the-Wild (ABAW) competition.
The paper presents Test-Time Adaptation via Cache Personalization (TTA‑CaP), a gradient‑free, cache‑based method that personalizes vision‑language models for facial expression recognition in videos. TTA‑CaP uses three complementary caches—a personalized static cache, a positive target cache, and a negative target cache—controlled by a tri‑gate mechanism to prevent corruption and provide robust subject‑matched evidence. Experiments on BioVid, StressID, and BAH datasets show that TTA‑CaP outperforms state‑of‑the‑art test‑time adaptation methods while keeping computational and memory overhead low.
arXiv:2608.28923v1 Announce Type: cross Abstract: Data augmentation is a cornerstone of deep learning pipelines, yet existing strategies treat it as a static, model-agnostic preprocessing step, eithe...
arXiv:2604. 11730v4 Announce Type: replace-cross Abstract: Using behavioural science, health interventions focus on behaviour change by providing a framework to help patients acquire and maintain healthy habits that improve medical outcomes.