D-GATNet: Interpretable Temporal Graph Attention Learning for ADHD Identification Using Dynamic Functional Connectivity
D-GATNet: Interpretable Temporal Graph Attention Learning for ADHD Identification Using Dynamic Functional Connectivity explores An interpretable temporal graph attention network for ADHD identification using dynamic brain connectivity, outperforming existing methods.. Commercial viability score: 7/10 in Medical AI.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisSpatio-Temporal Attention Graph Neural Network: Explaining Causalities With AttentionViability: 3.0
- Prior WorkMRC-GAT: A Meta-Relational Copula-Based Graph Attention Network for Interpretable Multimodal Alzheimer's Disease DiagnosisViability: 7.0
- Higher ViabilityGATE-AD: Graph Attention Network Encoding For Few-Shot Industrial Visual Anomaly DetectionViability: 8.0
- Higher ViabilityAdaptive Clinical-Aware Latent Diffusion for Multimodal Brain Image Generation and Missing Modality ImputationViability: 8.0
- Competing Approach