Segment-Level Risk Discovery in Online Handwriting for Alzheimer's Disease Detection

arXiv:2609.29384 · cs.CV, cs.AI · Submitted 2026-09-24 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-24

Updated: 2026-10-05

Terminology

• AUC — 12× in this paper · explained in 2 episodes
• Aggregation — 11× in this paper · explained in 1 episode
• Transformer — 8× in this paper · explained in 2 episodes
• Calibration — 7× in this paper · explained in 7 episodes
• Accuracy — 6× in this paper · explained in 1 episode
• Ablation — 5× in this paper · explained in 1 episode
• Representation Learning — 5× in this paper · explained in 2 episodes
• Interpretability — 4× in this paper · explained in 15 episodes
• Random Forest — 3× in this paper
• Transfer Learning — 3× in this paper · explained in 10 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Explainability — 2× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Abstraction — 1× in this paper · explained in 1 episode
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Information Leakage — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection
• Efficiently Modeling Long Sequences with Structured State Spaces
• Mamba: Linear-Time Sequence Modeling with Selective State Spaces
• Attention Is All You Need

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