← Back to all news
arXiv AI By Changqing Gong, Huafeng Qin, Mounim A. El-Yacoubi

Cross-Task Generalization in Handwriting-Based Alzheimer's Screening via Vision Language Adaptation

Read the original on arXiv AI →

The paper introduces a lightweight Cross‑Layer Fusion Adapter (CLFA) that adapts the CLIP vision‑language model for handwriting‑based Alzheimer's disease screening. CLFA inserts multi‑level adapters into a frozen visual encoder, fusing cross‑layer features with depthwise 2D convolutions to capture both local stroke irregularities and higher‑level handwriting structure. On the Darwin dataset, CLFA achieves 74.63% AUC, 74.85% accuracy, and 73.72% F1, outperforming the best competing model by 2.15, 1.79, and 1.87 percentage points across 600 task‑disjoint source‑target pairs.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv AI
6d ago

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

The paper introduces NormPaST‑Risk, a novel framework that detects Alzheimer’s disease from online handwriting by focusing on local, segment‑level risk rather than whole‑trajectory features. It employs a multi‑scale temporal encoder, a Paper‑Air state‑space model to separate on‑paper motor execution from in‑air planning, and a healthy‑normative branch to learn normal handwriting dynamics. A weakly supervised segment‑risk module identifies high‑risk handwriting segments, achieving superior AD/HC classification on the DARWIN benchmark and offering interpretable evidence linked to disease‑related handwriting changes.

By Changqing Gong, Huafeng Qin, Moun\^im A. El-Yacoubi
benchmarks
More like this →
arXiv AI
Jul 17

Parameter-efficient Prompt Tuning of Vision Foundation Model With Adaptive Focal Loss for Interpretable MCI Screening

arXiv:2607. 15047v1 Announce Type: cross Abstract: Mild Cognitive Impairment is a critical early stage of cognitive decline that frequently precedes Alzheimer's disease, yet its automated detection from neuropsychological drawing tests remains fundamentally constrained by data scarcity, class imbalance, and diagnostic ambiguity near clinical boundaries.

By Javad Khoramdel, Farhad Hoseyni, Amirhossein Nikoofard
ragfine-tuning
More like this →
arXiv AI
Sep 17

MINT: Multimodal Imaging-to-Speech Knowledge Transfer for Early Alzheimer's Screening

MINT (Multimodal Imaging-to-Speech Knowledge Transfer) is a three-stage framework that transfers MRI-derived biomarkers to speech representations for early Alzheimer’s screening. An MRI teacher creates a compact embedding space for CN‑versus‑MCI classification, and a residual projection head aligns speech features to this space using a geometric loss, allowing imaging‑free inference. Experiments on ADNI‑4 show that aligned speech matches speech baselines, while multimodal fusion outperforms MRI alone, and ablations highlight dropout regularization and self‑supervised pretraining as key design choices.

By Vrushank Ahire, Yogesh Kumar, Anouck Girard, M. A. Ganaie
ragmultimodal
More like this →
arXiv Computer Vision
Sep 14

A Multimodal Explainable Deep Learning Framework for Alzheimer's Disease Diagnosis using 3D Magnetic Resonance Imaging and Clinical Data

The study presents an explainable multimodal deep‑learning framework that combines a 3D CNN for T1‑weighted MRI with a feedforward network for harmonized clinical and demographic data to diagnose Alzheimer’s disease. Using 6,479 ADNI records and 1,703 OASIS‑3 records, the authors compare various model configurations on three‑way and pairwise diagnostic tasks, finding that performance and explanations vary by task, modality, fusion strategy, and cohort. SHAP and Integrated Gradients consistently highlight the MMSE score as the most influential tabular feature, while CAM‑based explanations differ across model setups and cohorts, indicating that explainability is not a stable property under cohort shift.

By Yusuf Brima, Marcellin Atemkeng, Lakshmana Rao Namamula, Antoine Vacavant
multimodal
More like this →