← Back to all news
arXiv Computer Vision By Yusuf Brima, Marcellin Atemkeng, Lakshmana Rao Namamula, Antoine Vacavant

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

Read the original on arXiv Computer Vision →

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.

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 Computer Vision.

Related stories

arXiv Computer Vision
Sep 7

A Generalizable Feature Extractor for Alzheimer's-Related Brain MRI Tasks

The study investigates whether a compact, supervised 3D CNN pretrained for brain‑age prediction can act as a reusable foundation model for various Alzheimer's‑related neuroimaging tasks. By freezing the 7.18 million weights and adding only ~1 % of trainable parameters via Low‑Rank Adaptation, the model achieved high performance across six experiments, including dementia classification, MCI progression prediction, amyloid positivity detection, and volume estimation of hippocampal and white matter hypointensities. The results demonstrate that the pretrained brain‑age model generalizes well to new datasets without retraining, offering a data‑efficient alternative to larger networks.

By Reza Rajabli, D. Louis Collins
fine-tuning
More like this →