ADHD/ADD

Predictors of schizophrenia spectrum disorders in early-onset first episodes of psychosis: a support vector machine model.

Identifying early-onset schizophrenia spectrum disorders (SSD) at a very early stage remains challenging. To assess the diagnostic predictive value of multiple types of data at the emergence of early-onset first-episode psychosis (FEP), various support vector machine (SVM) classifiers were developed. The data were from a 2-year, prospective, longitudinal study of 81 patients (age 9-17 years) with ...

Semantic Color Naturalness Breaker: Preventing Illegitimate Colorization via Content-Aware Color Priors

Automatic image colorization enables large-scale and low-cost reuse of grayscale media (e.g., manga panels and archival photographs), facilitating unauthorized reuse and redistribution. Once released online, grayscale content can be readily turned into unauthorized colorized derivatives using off-the-shelf models, creating a practical need for proactive, content-side protection at publication time...

TEDDY: A Pediatric Foundation Model for Risk Forewarning from ICD-Coded Diagnostic Histories

Pediatric electronic health records capture developmentally structured clinical trajectories, yet their potential for generative healthcare foundation...

Bi-PT: Bidirectional Cross-Attention Point Transformers for Four-Chamber Heart Reconstruction from Sparse Cardiac MRI Data

We propose Bi-PT, a pipeline for reconstructing 3D four-chamber human heart meshes from clinical sparsely sampled cardiac magnetic resonance imaging (...

Dictionaries, Not Darwin: Set-Level Selection Beats LLM Evolution in Scientific Equation Discovery

Large language models are increasingly used as evolutionary engines for scientific discovery: generate candidates, select winners, feed them back as p...

SABER: A Semantic-Aligned Brain Network Analysis Framework via Multi-scale Hypergraphs

Effective brain disease diagnosis requires the synergy of brain connectivity patterns and high-level semantic knowledge. Existing methods, however, la...

Integrating Genetic, Environmental, Cognitive, and Temperament Data for ADHD Prediction in Explainable Deep Learning Models

Objective: Attention-deficit/hyperactivity disorder (ADHD) is clinically and etiologically heterogeneous, and diagnostic decisions may benefit from in...

GRAPE: Graph-Augmented Prototype Explanations for Interactive Medical Image Diagnosis

Prototype-based medical image classifiers present three clinical limitations: they treat findings as independent, silently amplify unsafe physician fe...

Self-Supervised Behavioral Representations Across the Life Course: A Killifish Case Study

Self-supervised foundation models of aging are increasingly built from longitudinal data (biobanks, electronic health records, wearables) that is inhe...

Progressive Self-Supervised Learning with Individualized Community Assignment for Brain Network Analysis

Brain networks exhibit a modular community structure that varies across individuals and neurological conditions. However, existing self-supervised lea...

Latent Noise Mask for Reducing Visual Redundancy in Multimodal Large Language Models

Multimodal large language models (MLLMs) often fail in fine-grained visual reasoning, as question-relevant visual cues are diluted by dense and redund...

Prototype Latent World Model Replay for Class-Incremental Learning

Class-incremental learning requires a model to learn new classes while preserving decision regions for old ones. This is difficult when raw old sample...

Trait anxiety drives premature disengagement despite intact opportunity-cost sensitivity

Anxiety has been linked to difficulty sustaining engagement with ongoing tasks, even when continued engagement would yield greater rewards, yet the un...

Energy-Efficient CNN Acceleration with MSDF Digit-Serial Arithmetic on FPGA

This paper presents an energy-efficient hardware acceleration of the convolutional layers in the U-Net architecture for image segmentation, implemente...

Pulmonary Embolism Risk Stratification from CTPA and Medical Records: Vascular Graphs Are Not All You Need

Risk stratification for pulmonary embolism (PE) is critical for clinical decision-making. Stratification guidelines are based on patient medical recor...

Quantifying Explainable AI-introduced signal noise on ECG data with Spectral Entropy

Explainability techniques are used to assess the output of various deep learning models. This is especially true in healthcare, where models need to b...

Semantic Allocation in Ordered Bottlenecks: Predictive Residual Inference for Visual Representation Learning

Ordered bottlenecks aim to provide utility at flexible budgets by assigning coarse information to early tokens and task-relevant detail to later ones....

Zero-Shot Test-Time Canonicalization using Out-of-Distribution Scoring

Pretrained vision models often misclassify inputs that are rotated, scaled, or sheared, even though these affine transformations leave the object clas...

DR-Mamba: Automatic Inference-Time Domain Adaptation for Document Image Binarization via Sample-Conditioned Detail-Background Suppression

Degraded document image binarization is sensitive to domain shifts caused by paper aging, bleed-through, stains, shadows, and uneven illumination, and...

Risk Stratification for ICU Delirium using Pervasive Ambient Sensing Information

Delirium is a common and serious complication in the Intensive Care Unit (ICU), associated with increased morbidity, prolonged hospital stays, and hig...